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  • v.37(16); 2022 Apr 25

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Quantitative research questionsQuantitative research hypotheses
Descriptive research questionsSimple hypothesis
Comparative research questionsComplex hypothesis
Relationship research questionsDirectional hypothesis
Non-directional hypothesis
Associative hypothesis
Causal hypothesis
Null hypothesis
Alternative hypothesis
Working hypothesis
Statistical hypothesis
Logical hypothesis
Hypothesis-testing
Qualitative research questionsQualitative research hypotheses
Contextual research questionsHypothesis-generating
Descriptive research questions
Evaluation research questions
Explanatory research questions
Exploratory research questions
Generative research questions
Ideological research questions
Ethnographic research questions
Phenomenological research questions
Grounded theory questions
Qualitative case study questions

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Quantitative research questions
Descriptive research question
- Measures responses of subjects to variables
- Presents variables to measure, analyze, or assess
What is the proportion of resident doctors in the hospital who have mastered ultrasonography (response of subjects to a variable) as a diagnostic technique in their clinical training?
Comparative research question
- Clarifies difference between one group with outcome variable and another group without outcome variable
Is there a difference in the reduction of lung metastasis in osteosarcoma patients who received the vitamin D adjunctive therapy (group with outcome variable) compared with osteosarcoma patients who did not receive the vitamin D adjunctive therapy (group without outcome variable)?
- Compares the effects of variables
How does the vitamin D analogue 22-Oxacalcitriol (variable 1) mimic the antiproliferative activity of 1,25-Dihydroxyvitamin D (variable 2) in osteosarcoma cells?
Relationship research question
- Defines trends, association, relationships, or interactions between dependent variable and independent variable
Is there a relationship between the number of medical student suicide (dependent variable) and the level of medical student stress (independent variable) in Japan during the first wave of the COVID-19 pandemic?

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Quantitative research hypotheses
Simple hypothesis
- Predicts relationship between single dependent variable and single independent variable
If the dose of the new medication (single independent variable) is high, blood pressure (single dependent variable) is lowered.
Complex hypothesis
- Foretells relationship between two or more independent and dependent variables
The higher the use of anticancer drugs, radiation therapy, and adjunctive agents (3 independent variables), the higher would be the survival rate (1 dependent variable).
Directional hypothesis
- Identifies study direction based on theory towards particular outcome to clarify relationship between variables
Privately funded research projects will have a larger international scope (study direction) than publicly funded research projects.
Non-directional hypothesis
- Nature of relationship between two variables or exact study direction is not identified
- Does not involve a theory
Women and men are different in terms of helpfulness. (Exact study direction is not identified)
Associative hypothesis
- Describes variable interdependency
- Change in one variable causes change in another variable
A larger number of people vaccinated against COVID-19 in the region (change in independent variable) will reduce the region’s incidence of COVID-19 infection (change in dependent variable).
Causal hypothesis
- An effect on dependent variable is predicted from manipulation of independent variable
A change into a high-fiber diet (independent variable) will reduce the blood sugar level (dependent variable) of the patient.
Null hypothesis
- A negative statement indicating no relationship or difference between 2 variables
There is no significant difference in the severity of pulmonary metastases between the new drug (variable 1) and the current drug (variable 2).
Alternative hypothesis
- Following a null hypothesis, an alternative hypothesis predicts a relationship between 2 study variables
The new drug (variable 1) is better on average in reducing the level of pain from pulmonary metastasis than the current drug (variable 2).
Working hypothesis
- A hypothesis that is initially accepted for further research to produce a feasible theory
Dairy cows fed with concentrates of different formulations will produce different amounts of milk.
Statistical hypothesis
- Assumption about the value of population parameter or relationship among several population characteristics
- Validity tested by a statistical experiment or analysis
The mean recovery rate from COVID-19 infection (value of population parameter) is not significantly different between population 1 and population 2.
There is a positive correlation between the level of stress at the workplace and the number of suicides (population characteristics) among working people in Japan.
Logical hypothesis
- Offers or proposes an explanation with limited or no extensive evidence
If healthcare workers provide more educational programs about contraception methods, the number of adolescent pregnancies will be less.
Hypothesis-testing (Quantitative hypothesis-testing research)
- Quantitative research uses deductive reasoning.
- This involves the formation of a hypothesis, collection of data in the investigation of the problem, analysis and use of the data from the investigation, and drawing of conclusions to validate or nullify the hypotheses.

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative research questions
Contextual research question
- Ask the nature of what already exists
- Individuals or groups function to further clarify and understand the natural context of real-world problems
What are the experiences of nurses working night shifts in healthcare during the COVID-19 pandemic? (natural context of real-world problems)
Descriptive research question
- Aims to describe a phenomenon
What are the different forms of disrespect and abuse (phenomenon) experienced by Tanzanian women when giving birth in healthcare facilities?
Evaluation research question
- Examines the effectiveness of existing practice or accepted frameworks
How effective are decision aids (effectiveness of existing practice) in helping decide whether to give birth at home or in a healthcare facility?
Explanatory research question
- Clarifies a previously studied phenomenon and explains why it occurs
Why is there an increase in teenage pregnancy (phenomenon) in Tanzania?
Exploratory research question
- Explores areas that have not been fully investigated to have a deeper understanding of the research problem
What factors affect the mental health of medical students (areas that have not yet been fully investigated) during the COVID-19 pandemic?
Generative research question
- Develops an in-depth understanding of people’s behavior by asking ‘how would’ or ‘what if’ to identify problems and find solutions
How would the extensive research experience of the behavior of new staff impact the success of the novel drug initiative?
Ideological research question
- Aims to advance specific ideas or ideologies of a position
Are Japanese nurses who volunteer in remote African hospitals able to promote humanized care of patients (specific ideas or ideologies) in the areas of safe patient environment, respect of patient privacy, and provision of accurate information related to health and care?
Ethnographic research question
- Clarifies peoples’ nature, activities, their interactions, and the outcomes of their actions in specific settings
What are the demographic characteristics, rehabilitative treatments, community interactions, and disease outcomes (nature, activities, their interactions, and the outcomes) of people in China who are suffering from pneumoconiosis?
Phenomenological research question
- Knows more about the phenomena that have impacted an individual
What are the lived experiences of parents who have been living with and caring for children with a diagnosis of autism? (phenomena that have impacted an individual)
Grounded theory question
- Focuses on social processes asking about what happens and how people interact, or uncovering social relationships and behaviors of groups
What are the problems that pregnant adolescents face in terms of social and cultural norms (social processes), and how can these be addressed?
Qualitative case study question
- Assesses a phenomenon using different sources of data to answer “why” and “how” questions
- Considers how the phenomenon is influenced by its contextual situation.
How does quitting work and assuming the role of a full-time mother (phenomenon assessed) change the lives of women in Japan?
Qualitative research hypotheses
Hypothesis-generating (Qualitative hypothesis-generating research)
- Qualitative research uses inductive reasoning.
- This involves data collection from study participants or the literature regarding a phenomenon of interest, using the collected data to develop a formal hypothesis, and using the formal hypothesis as a framework for testing the hypothesis.
- Qualitative exploratory studies explore areas deeper, clarifying subjective experience and allowing formulation of a formal hypothesis potentially testable in a future quantitative approach.

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

VariablesUnclear and weak statement (Statement 1) Clear and good statement (Statement 2) Points to avoid
Research questionWhich is more effective between smoke moxibustion and smokeless moxibustion?“Moreover, regarding smoke moxibustion versus smokeless moxibustion, it remains unclear which is more effective, safe, and acceptable to pregnant women, and whether there is any difference in the amount of heat generated.” 1) Vague and unfocused questions
2) Closed questions simply answerable by yes or no
3) Questions requiring a simple choice
HypothesisThe smoke moxibustion group will have higher cephalic presentation.“Hypothesis 1. The smoke moxibustion stick group (SM group) and smokeless moxibustion stick group (-SLM group) will have higher rates of cephalic presentation after treatment than the control group.1) Unverifiable hypotheses
Hypothesis 2. The SM group and SLM group will have higher rates of cephalic presentation at birth than the control group.2) Incompletely stated groups of comparison
Hypothesis 3. There will be no significant differences in the well-being of the mother and child among the three groups in terms of the following outcomes: premature birth, premature rupture of membranes (PROM) at < 37 weeks, Apgar score < 7 at 5 min, umbilical cord blood pH < 7.1, admission to neonatal intensive care unit (NICU), and intrauterine fetal death.” 3) Insufficiently described variables or outcomes
Research objectiveTo determine which is more effective between smoke moxibustion and smokeless moxibustion.“The specific aims of this pilot study were (a) to compare the effects of smoke moxibustion and smokeless moxibustion treatments with the control group as a possible supplement to ECV for converting breech presentation to cephalic presentation and increasing adherence to the newly obtained cephalic position, and (b) to assess the effects of these treatments on the well-being of the mother and child.” 1) Poor understanding of the research question and hypotheses
2) Insufficient description of population, variables, or study outcomes

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

VariablesUnclear and weak statement (Statement 1)Clear and good statement (Statement 2)Points to avoid
Research questionDoes disrespect and abuse (D&A) occur in childbirth in Tanzania?How does disrespect and abuse (D&A) occur and what are the types of physical and psychological abuses observed in midwives’ actual care during facility-based childbirth in urban Tanzania?1) Ambiguous or oversimplistic questions
2) Questions unverifiable by data collection and analysis
HypothesisDisrespect and abuse (D&A) occur in childbirth in Tanzania.Hypothesis 1: Several types of physical and psychological abuse by midwives in actual care occur during facility-based childbirth in urban Tanzania.1) Statements simply expressing facts
Hypothesis 2: Weak nursing and midwifery management contribute to the D&A of women during facility-based childbirth in urban Tanzania.2) Insufficiently described concepts or variables
Research objectiveTo describe disrespect and abuse (D&A) in childbirth in Tanzania.“This study aimed to describe from actual observations the respectful and disrespectful care received by women from midwives during their labor period in two hospitals in urban Tanzania.” 1) Statements unrelated to the research question and hypotheses
2) Unattainable or unexplorable objectives

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

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Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

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EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

  • EXAMPLE 1. Descriptive research question (quantitative research)
  • - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
  • “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
  • RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
  • EXAMPLE 2. Relationship research question (quantitative research)
  • - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
  • “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
  • Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
  • EXAMPLE 3. Comparative research question (quantitative research)
  • - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
  • “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
  • RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
  • STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
  • EXAMPLE 4. Exploratory research question (qualitative research)
  • - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
  • “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
  • EXAMPLE 5. Relationship research question (quantitative research)
  • - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
  • “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

  • EXAMPLE 1. Working hypothesis (quantitative research)
  • - A hypothesis that is initially accepted for further research to produce a feasible theory
  • “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
  • “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
  • EXAMPLE 2. Exploratory hypothesis (qualitative research)
  • - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
  • “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
  • “Conclusion
  • Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
  • EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
  • “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
  • Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
  • EXAMPLE 4. Statistical hypothesis (quantitative research)
  • - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
  • “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
  • “Statistical Analysis
  • ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

  • EXAMPLE 1. Background, hypotheses, and aims are provided
  • “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
  • “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
  • “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
  • EXAMPLE 2. Background, hypotheses, and aims are provided
  • “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
  • “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
  • EXAMPLE 3. Background, aim, and hypothesis are provided
  • “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
  • “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
  • “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

  • Conceptualization: Barroga E, Matanguihan GJ.
  • Methodology: Barroga E, Matanguihan GJ.
  • Writing - original draft: Barroga E, Matanguihan GJ.
  • Writing - review & editing: Barroga E, Matanguihan GJ.

Grad Coach

How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

  • What exactly the results chapter is
  • What you need to include in your chapter
  • How to structure the chapter
  • Tips and tricks for writing a top-notch chapter
  • Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

Free template for results section of a dissertation or thesis

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

There must be alignment between your research aims objectives and questions

As a general guide, your results chapter will typically include the following:

  • Some demographic data about your sample
  • Reliability tests (if you used measurement scales)
  • Descriptive statistics
  • Inferential statistics (if your research objectives and questions require these)
  • Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

summary of findings in quantitative research

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Your chapter must have a golden thread

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

  • What age range are they?
  • How is gender distributed?
  • How is ethnicity distributed?
  • What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Descriptive statistics

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

  • The mean – this is simply the mathematical average of a range of numbers.
  • The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
  • The mode – this is the most commonly repeated number in the data set.
  • Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
  • Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
  • Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Dive into the inferential statistics

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

make it easy for your reader to understand your quantitative results

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

  • Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
  • Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
  • Set your significance level (this is usually 0.05)
  • Calculate your statistics and find your p-value (e.g., p=0.01)
  • Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

  • When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
  • Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
  • Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
  • Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

summary of findings in quantitative research

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Thank you. I will try my best to write my results.

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Awesome content 👏🏾

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this was great explaination

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Quantitative Data Analysis

9 Presenting the Results of Quantitative Analysis

Mikaila Mariel Lemonik Arthur

This chapter provides an overview of how to present the results of quantitative analysis, in particular how to create effective tables for displaying quantitative results and how to write quantitative research papers that effectively communicate the methods used and findings of quantitative analysis.

Writing the Quantitative Paper

Standard quantitative social science papers follow a specific format. They begin with a title page that includes a descriptive title, the author(s)’ name(s), and a 100 to 200 word abstract that summarizes the paper. Next is an introduction that makes clear the paper’s research question, details why this question is important, and previews what the paper will do. After that comes a literature review, which ends with a summary of the research question(s) and/or hypotheses. A methods section, which explains the source of data, sample, and variables and quantitative techniques used, follows. Many analysts will include a short discussion of their descriptive statistics in the methods section. A findings section details the findings of the analysis, supported by a variety of tables, and in some cases graphs, all of which are explained in the text. Some quantitative papers, especially those using more complex techniques, will include equations. Many papers follow the findings section with a discussion section, which provides an interpretation of the results in light of both the prior literature and theory presented in the literature review and the research questions/hypotheses. A conclusion ends the body of the paper. This conclusion should summarize the findings, answering the research questions and stating whether any hypotheses were supported, partially supported, or not supported. Limitations of the research are detailed. Papers typically include suggestions for future research, and where relevant, some papers include policy implications. After the body of the paper comes the works cited; some papers also have an Appendix that includes additional tables and figures that did not fit into the body of the paper or additional methodological details. While this basic format is similar for papers regardless of the type of data they utilize, there are specific concerns relating to quantitative research in terms of the methods and findings that will be discussed here.

In the methods section, researchers clearly describe the methods they used to obtain and analyze the data for their research. When relying on data collected specifically for a given paper, researchers will need to discuss the sample and data collection; in most cases, though, quantitative research relies on pre-existing datasets. In these cases, researchers need to provide information about the dataset, including the source of the data, the time it was collected, the population, and the sample size. Regardless of the source of the data, researchers need to be clear about which variables they are using in their research and any transformations or manipulations of those variables. They also need to explain the specific quantitative techniques that they are using in their analysis; if different techniques are used to test different hypotheses, this should be made clear. In some cases, publications will require that papers be submitted along with any code that was used to produce the analysis (in SPSS terms, the syntax files), which more advanced researchers will usually have on hand. In many cases, basic descriptive statistics are presented in tabular form and explained within the methods section.

The findings sections of quantitative papers are organized around explaining the results as shown in tables and figures. Not all results are depicted in tables and figures—some minor or null findings will simply be referenced—but tables and figures should be produced for all findings to be discussed at any length. If there are too many tables and figures, some can be moved to an appendix after the body of the text and referred to in the text (e.g. “See Table 12 in Appendix A”).

Discussions of the findings should not simply restate the contents of the table. Rather, they should explain and interpret it for readers, and they should do so in light of the hypothesis or hypotheses that are being tested. Conclusions—discussions of whether the hypothesis or hypotheses are supported or not supported—should wait for the conclusion of the paper.

Creating Effective Tables

When creating tables to display the results of quantitative analysis, the most important goals are to create tables that are clear and concise but that also meet standard conventions in the field. This means, first of all, paring down the volume of information produced in the statistical output to just include the information most necessary for interpreting the results, but doing so in keeping with standard table conventions. It also means making tables that are well-formatted and designed, so that readers can understand what the tables are saying without struggling to find information. For example, tables (as well as figures such as graphs) need clear captions; they are typically numbered and referred to by number in the text. Columns and rows should have clear headings. Depending on the content of the table, formatting tools may need to be used to set off header rows/columns and/or total rows/columns; cell-merging tools may be necessary; and shading may be important in tables with many rows or columns.

Here, you will find some instructions for creating tables of results from descriptive, crosstabulation, correlation, and regression analysis that are clear, concise, and meet normal standards for data display in social science. In addition, after the instructions for creating tables, you will find an example of how a paper incorporating each table might describe that table in the text.

Descriptive Statistics

When presenting the results of descriptive statistics, we create one table with columns for each type of descriptive statistic and rows for each variable. Note, of course, that depending on level of measurement only certain descriptive statistics are appropriate for a given variable, so there may be many cells in the table marked with an — to show that this statistic is not calculated for this variable. So, consider the set of descriptive statistics below, for occupational prestige, age, highest degree earned, and whether the respondent was born in this country.

Table 1. SPSS Ouput: Selected Descriptive Statistics
Statistics
R’s occupational prestige score (2010) Age of respondent
N Valid 3873 3699
Missing 159 333
Mean 46.54 52.16
Median 47.00 53.00
Std. Deviation 13.811 17.233
Variance 190.745 296.988
Skewness .141 .018
Std. Error of Skewness .039 .040
Kurtosis -.809 -1.018
Std. Error of Kurtosis .079 .080
Range 64 71
Minimum 16 18
Maximum 80 89
Percentiles 25 35.00 37.00
50 47.00 53.00
75 59.00 66.00
Statistics
R’s highest degree
N Valid 4009
Missing 23
Median 2.00
Mode 1
Range 4
Minimum 0
Maximum 4
R’s highest degree
Frequency Percent Valid Percent Cumulative Percent
Valid less than high school 246 6.1 6.1 6.1
high school 1597 39.6 39.8 46.0
associate/junior college 370 9.2 9.2 55.2
bachelor’s 1036 25.7 25.8 81.0
graduate 760 18.8 19.0 100.0
Total 4009 99.4 100.0
Missing System 23 .6
Total 4032 100.0
Statistics
Was r born in this country
N Valid 3960
Missing 72
Mean 1.11
Mode 1
Was r born in this country
Frequency Percent Valid Percent Cumulative Percent
Valid yes 3516 87.2 88.8 88.8
no 444 11.0 11.2 100.0
Total 3960 98.2 100.0
Missing System 72 1.8
Total 4032 100.0

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

Table 2. Descriptive Statistics
46.54 52.16 1.11
47 53 1: Associates (9.2%) 1: Yes (88.8%)
2: High School (39.8%)
13.811 17.233
190.745 296.988
0.141 0.018
-0.809 -1.018
64 (16-80) 71 (18-89) Less than High School (0) –  Graduate (4)
35-59 37-66
3873 3699 4009 3960

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 3. R’s highest degree * R’s subjective class identification Crosstabulation
R’s subjective class identification Total
lower class working class middle class upper class
R’s highest degree less than high school Count 65 106 68 7 246
% within R’s subjective class identification 18.8% 7.1% 3.4% 4.2% 6.2%
high school Count 217 800 551 23 1591
% within R’s subjective class identification 62.9% 53.7% 27.6% 13.9% 39.8%
associate/junior college Count 30 191 144 3 368
% within R’s subjective class identification 8.7% 12.8% 7.2% 1.8% 9.2%
bachelor’s Count 27 269 686 49 1031
% within R’s subjective class identification 7.8% 18.1% 34.4% 29.5% 25.8%
graduate Count 6 123 546 84 759
% within R’s subjective class identification 1.7% 8.3% 27.4% 50.6% 19.0%
Total Count 345 1489 1995 166 3995
% within R’s subjective class identification 100.0% 100.0% 100.0% 100.0% 100.0%
Chi-Square Tests
Value df Asymptotic Significance (2-sided)
Pearson Chi-Square 819.579 12 <.001
Likelihood Ratio 839.200 12 <.001
Linear-by-Linear Association 700.351 1 <.001
N of Valid Cases 3995
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 10.22.
Symmetric Measures
Value Asymptotic Standard Error Approximate T Approximate Significance
Interval by Interval Pearson’s R .419 .013 29.139 <.001
Ordinal by Ordinal Spearman Correlation .419 .013 29.158 <.001
N of Valid Cases 3995
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

 
18.8% 7.1% 3.4% 4.2% 6.2%
62.9% 53.7% 27.6% 13.9% 39.8%
8.7% 12.8% 7.2% 1.8% 9.2%
7.8% 18.1% 34.4% 29.5% 25.8%
1.7% 8.3% 27.4% 50.6% 19.0%
N: 3995 Spearman Correlation 0.419***

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 5. SPSS Output: Correlations
Age of respondent R’s occupational prestige score (2010) Highest year of school R completed R’s family income in 1986 dollars
Age of respondent Pearson Correlation 1 .087 .014 .017
Sig. (2-tailed) <.001 .391 .314
N 3699 3571 3683 3336
R’s occupational prestige score (2010) Pearson Correlation .087 1 .504 .316
Sig. (2-tailed) <.001 <.001 <.001
N 3571 3873 3817 3399
Highest year of school R completed Pearson Correlation .014 .504 1 .360
Sig. (2-tailed) .391 <.001 <.001
N 3683 3817 3966 3497
R’s family income in 1986 dollars Pearson Correlation .017 .316 .360 1
Sig. (2-tailed) .314 <.001 <.001
N 3336 3399 3497 3509
**. Correlation is significant at the 0.01 level (2-tailed).

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

Table 6. Correlation Matrix
1
0.087*** 1
0.014 0.504*** 1
0.017 0.316*** 0.360*** 1

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

Table 7. SPSS Output: Regression
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .395 .156 .155 36729.04841
a. Predictors: (Constant), Highest year of school R completed, Age of respondent, R’s occupational prestige score (2010)
ANOVA
Model Sum of Squares df Mean Square F Sig.
1 Regression 805156927306.583 3 268385642435.528 198.948 <.001
Residual 4351948187487.015 3226 1349022996.741
Total 5157105114793.598 3229
a. Dependent Variable: R’s family income in 1986 dollars
b. Predictors: (Constant), Highest year of school R completed, Age of respondent, R’s occupational prestige score (2010)
Coefficients
Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) -44403.902 4166.576 -10.657 <.001
Age of respondent 9.547 38.733 .004 .246 .805 .993 1.007
R’s occupational prestige score (2010) 522.887 54.327 .181 9.625 <.001 .744 1.345
Highest year of school R completed 3988.545 274.039 .272 14.555 <.001 .747 1.339
a. Dependent Variable: R’s family income in 1986 dollars

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

Table 8. Regression Results for Dependent Variable Family Income in 1986 Dollars
Age 0.004
(38.733)
Occupational Prestige Score 0.181***
(54.327)
Highest Year of School Completed 0.272***
(274.039)
Degrees of Freedom 3229
Constant -44,403.902

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]
  • Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
  • Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
  • Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
  • Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
  • Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
  • Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
  • Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 14: completing ‘summary of findings’ tables and grading the certainty of the evidence.

Holger J Schünemann, Julian PT Higgins, Gunn E Vist, Paul Glasziou, Elie A Akl, Nicole Skoetz, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group (formerly Applicability and Recommendations Methods Group) and the Cochrane Statistical Methods Group

Key Points:

  • A ‘Summary of findings’ table for a given comparison of interventions provides key information concerning the magnitudes of relative and absolute effects of the interventions examined, the amount of available evidence and the certainty (or quality) of available evidence.
  • ‘Summary of findings’ tables include a row for each important outcome (up to a maximum of seven). Accepted formats of ‘Summary of findings’ tables and interactive ‘Summary of findings’ tables can be produced using GRADE’s software GRADEpro GDT.
  • Cochrane has adopted the GRADE approach (Grading of Recommendations Assessment, Development and Evaluation) for assessing certainty (or quality) of a body of evidence.
  • The GRADE approach specifies four levels of the certainty for a body of evidence for a given outcome: high, moderate, low and very low.
  • GRADE assessments of certainty are determined through consideration of five domains: risk of bias, inconsistency, indirectness, imprecision and publication bias. For evidence from non-randomized studies and rarely randomized studies, assessments can then be upgraded through consideration of three further domains.

Cite this chapter as: Schünemann HJ, Higgins JPT, Vist GE, Glasziou P, Akl EA, Skoetz N, Guyatt GH. Chapter 14: Completing ‘Summary of findings’ tables and grading the certainty of the evidence. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

14.1 ‘Summary of findings’ tables

14.1.1 introduction to ‘summary of findings’ tables.

‘Summary of findings’ tables present the main findings of a review in a transparent, structured and simple tabular format. In particular, they provide key information concerning the certainty or quality of evidence (i.e. the confidence or certainty in the range of an effect estimate or an association), the magnitude of effect of the interventions examined, and the sum of available data on the main outcomes. Cochrane Reviews should incorporate ‘Summary of findings’ tables during planning and publication, and should have at least one key ‘Summary of findings’ table representing the most important comparisons. Some reviews may include more than one ‘Summary of findings’ table, for example if the review addresses more than one major comparison, or includes substantially different populations that require separate tables (e.g. because the effects differ or it is important to show results separately). In the Cochrane Database of Systematic Reviews (CDSR),  all ‘Summary of findings’ tables for a review appear at the beginning, before the Background section.

14.1.2 Selecting outcomes for ‘Summary of findings’ tables

Planning for the ‘Summary of findings’ table starts early in the systematic review, with the selection of the outcomes to be included in: (i) the review; and (ii) the ‘Summary of findings’ table. This is a crucial step, and one that review authors need to address carefully.

To ensure production of optimally useful information, Cochrane Reviews begin by developing a review question and by listing all main outcomes that are important to patients and other decision makers (see Chapter 2 and Chapter 3 ). The GRADE approach to assessing the certainty of the evidence (see Section 14.2 ) defines and operationalizes a rating process that helps separate outcomes into those that are critical, important or not important for decision making. Consultation and feedback on the review protocol, including from consumers and other decision makers, can enhance this process.

Critical outcomes are likely to include clearly important endpoints; typical examples include mortality and major morbidity (such as strokes and myocardial infarction). However, they may also represent frequent minor and rare major side effects, symptoms, quality of life, burdens associated with treatment, and resource issues (costs). Burdens represent the impact of healthcare workload on patient function and well-being, and include the demands of adhering to an intervention that patients or caregivers (e.g. family) may dislike, such as having to undergo more frequent tests, or the restrictions on lifestyle that certain interventions require (Spencer-Bonilla et al 2017).

Frequently, when formulating questions that include all patient-important outcomes for decision making, review authors will confront reports of studies that have not included all these outcomes. This is particularly true for adverse outcomes. For instance, randomized trials might contribute evidence on intended effects, and on frequent, relatively minor side effects, but not report on rare adverse outcomes such as suicide attempts. Chapter 19 discusses strategies for addressing adverse effects. To obtain data for all important outcomes it may be necessary to examine the results of non-randomized studies (see Chapter 24 ). Cochrane, in collaboration with others, has developed guidance for review authors to support their decision about when to look for and include non-randomized studies (Schünemann et al 2013).

If a review includes only randomized trials, these trials may not address all important outcomes and it may therefore not be possible to address these outcomes within the constraints of the review. Review authors should acknowledge these limitations and make them transparent to readers. Review authors are encouraged to include non-randomized studies to examine rare or long-term adverse effects that may not adequately be studied in randomized trials. This raises the possibility that harm outcomes may come from studies in which participants differ from those in studies used in the analysis of benefit. Review authors will then need to consider how much such differences are likely to impact on the findings, and this will influence the certainty of evidence because of concerns about indirectness related to the population (see Section 14.2.2 ).

Non-randomized studies can provide important information not only when randomized trials do not report on an outcome or randomized trials suffer from indirectness, but also when the evidence from randomized trials is rated as very low and non-randomized studies provide evidence of higher certainty. Further discussion of these issues appears also in Chapter 24 .

14.1.3 General template for ‘Summary of findings’ tables

Several alternative standard versions of ‘Summary of findings’ tables have been developed to ensure consistency and ease of use across reviews, inclusion of the most important information needed by decision makers, and optimal presentation (see examples at Figures 14.1.a and 14.1.b ). These formats are supported by research that focused on improved understanding of the information they intend to convey (Carrasco-Labra et al 2016, Langendam et al 2016, Santesso et al 2016). They are available through GRADE’s official software package developed to support the GRADE approach: GRADEpro GDT (www.gradepro.org).

Standard Cochrane ‘Summary of findings’ tables include the following elements using one of the accepted formats. Further guidance on each of these is provided in Section 14.1.6 .

  • A brief description of the population and setting addressed by the available evidence (which may be slightly different to or narrower than those defined by the review question).
  • A brief description of the comparison addressed in the ‘Summary of findings’ table, including both the experimental and comparison interventions.
  • A list of the most critical and/or important health outcomes, both desirable and undesirable, limited to seven or fewer outcomes.
  • A measure of the typical burden of each outcomes (e.g. illustrative risk, or illustrative mean, on comparator intervention).
  • The absolute and relative magnitude of effect measured for each (if both are appropriate).
  • The numbers of participants and studies contributing to the analysis of each outcomes.
  • A GRADE assessment of the overall certainty of the body of evidence for each outcome (which may vary by outcome).
  • Space for comments.
  • Explanations (formerly known as footnotes).

Ideally, ‘Summary of findings’ tables are supported by more detailed tables (known as ‘evidence profiles’) to which the review may be linked, which provide more detailed explanations. Evidence profiles include the same important health outcomes, and provide greater detail than ‘Summary of findings’ tables of both of the individual considerations feeding into the grading of certainty and of the results of the studies (Guyatt et al 2011a). They ensure that a structured approach is used to rating the certainty of evidence. Although they are rarely published in Cochrane Reviews, evidence profiles are often used, for example, by guideline developers in considering the certainty of the evidence to support guideline recommendations. Review authors will find it easier to develop the ‘Summary of findings’ table by completing the rating of the certainty of evidence in the evidence profile first in GRADEpro GDT. They can then automatically convert this to one of the ‘Summary of findings’ formats in GRADEpro GDT, including an interactive ‘Summary of findings’ for publication.

As a measure of the magnitude of effect for dichotomous outcomes, the ‘Summary of findings’ table should provide a relative measure of effect (e.g. risk ratio, odds ratio, hazard) and measures of absolute risk. For other types of data, an absolute measure alone (such as a difference in means for continuous data) might be sufficient. It is important that the magnitude of effect is presented in a meaningful way, which may require some transformation of the result of a meta-analysis (see also Chapter 15, Section 15.4 and Section 15.5 ). Reviews with more than one main comparison should include a separate ‘Summary of findings’ table for each comparison.

Figure 14.1.a provides an example of a ‘Summary of findings’ table. Figure 15.1.b  provides an alternative format that may further facilitate users’ understanding and interpretation of the review’s findings. Evidence evaluating different formats suggests that the ‘Summary of findings’ table should include a risk difference as a measure of the absolute effect and authors should preferably use a format that includes a risk difference .

A detailed description of the contents of a ‘Summary of findings’ table appears in Section 14.1.6 .

Figure 14.1.a Example of a ‘Summary of findings’ table

Summary of findings (for interactive version click here )

anyone taking a long flight (lasting more than 6 hours)

international air travel

compression stockings

without stockings

Outcomes

* (95% CI)

Relative effect (95% CI)

Number of participants (studies)

Certainty of the evidence (GRADE)

Comments

Assumed risk

Corresponding risk

(DVT)

See comment

See comment

Not estimable

2821

(9 studies)

See comment

0 participants developed symptomatic DVT in these studies

(0.04 to 0.26)

2637

(9 studies)

⊕⊕⊕⊕

 

(0 to 3)

(1 to 8)

(2 to 15)

(0.18 to 1.13)

1804

(8 studies)

⊕⊕⊕◯

 

Post-flight values measured on a scale from 0, no oedema, to 10, maximum oedema

The mean oedema score ranged across control groups from

The mean oedema score in the intervention groups was on average

(95% CI –4.9 to –4.5)

 

1246

(6 studies)

⊕⊕◯◯

 

See comment

See comment

Not estimable

2821

(9 studies)

See comment

0 participants developed pulmonary embolus in these studies

See comment

See comment

Not estimable

2821

(9 studies)

See comment

0 participants died in these studies

See comment

See comment

Not estimable

1182

(4 studies)

See comment

The tolerability of the stockings was described as very good with no complaints of side effects in 4 studies

*The basis for the is provided in footnotes. The (and its 95% confidence interval) is based on the assumed risk in the intervention group and the of the intervention (and its 95% CI).

CI: confidence interval; RR: risk ratio; GRADE: GRADE Working Group grades of evidence (see explanations).

a All the stockings in the nine studies included in this review were below-knee compression stockings. In four studies the compression strength was 20 mmHg to 30 mmHg at the ankle. It was 10 mmHg to 20 mmHg in the other four studies. Stockings come in different sizes. If a stocking is too tight around the knee it can prevent essential venous return causing the blood to pool around the knee. Compression stockings should be fitted properly. A stocking that is too tight could cut into the skin on a long flight and potentially cause ulceration and increased risk of DVT. Some stockings can be slightly thicker than normal leg covering and can be potentially restrictive with tight foot wear. It is a good idea to wear stockings around the house prior to travel to ensure a good, comfortable fit. Participants put their stockings on two to three hours before the flight in most of the studies. The availability and cost of stockings can vary.

b Two studies recruited high risk participants defined as those with previous episodes of DVT, coagulation disorders, severe obesity, limited mobility due to bone or joint problems, neoplastic disease within the previous two years, large varicose veins or, in one of the studies, participants taller than 190 cm and heavier than 90 kg. The incidence for the seven studies that excluded high risk participants was 1.45% and the incidence for the two studies that recruited high-risk participants (with at least one risk factor) was 2.43%. We have used 10 and 30 per 1000 to express different risk strata, respectively.

c The confidence interval crosses no difference and does not rule out a small increase.

d The measurement of oedema was not validated (indirectness of the outcome) or blinded to the intervention (risk of bias).

e If there are very few or no events and the number of participants is large, judgement about the certainty of evidence (particularly judgements about imprecision) may be based on the absolute effect. Here the certainty rating may be considered ‘high’ if the outcome was appropriately assessed and the event, in fact, did not occur in 2821 studied participants.

f None of the other studies reported adverse effects, apart from four cases of superficial vein thrombosis in varicose veins in the knee region that were compressed by the upper edge of the stocking in one study.

Figure 14.1.b Example of alternative ‘Summary of findings’ table

children given antibiotics

inpatients and outpatient

probiotics

no probiotics

Follow-up: 10 days to 3 months

Children < 5 years

 

⊕⊕⊕⊝

Due to risk of bias

Probably decreases the incidence of diarrhoea.

1474 (7 studies)

(0.29 to 0.55)

(6.5 to 12.2)

(10.1 to 15.8 fewer)

Children > 5 years

 

⊕⊕⊝⊝

Due to risk of bias and imprecision

May decrease the incidence of diarrhoea.

624 (4 studies)

(0.53 to 1.21)

(5.9 to 13.6)

(5.3 fewer to 2.4 more)

Follow-up: 10 to 44 days

1575 (11 studies)

-

(0.8 to 3.8)

(1 fewer to 2 more)

⊕⊕⊝⊝

Due to risk of bias and inconsistency

There may be little or no difference in adverse events.

Follow-up: 10 days to 3 months

897 (5 studies)

-

The mean duration of diarrhoea without probiotics was

-

(1.18 to 0.02 fewer days)

⊕⊕⊝⊝

Due to imprecision and inconsistency

May decrease the duration of diarrhoea.

Follow-up: 10 days to 3 months

425 (4 studies)

-

The mean stools per day without probiotics was

-

(0.6 to 0 fewer)

⊕⊕⊝⊝

Due to imprecision and inconsistency

There may be little or no difference in stools per day.

*The basis for the (e.g. the median control group risk across studies) is provided in footnotes. The (and its 95% confidence interval) is based on the assumed risk in the comparison group and the of the intervention (and its 95% CI). confidence interval; risk ratio.

Control group risk estimates come from pooled estimates of control groups. Relative effect based on available case analysis

High risk of bias due to high loss to follow-up.

Imprecision due to few events and confidence intervals include appreciable benefit or harm.

Side effects: rash, nausea, flatulence, vomiting, increased phlegm, chest pain, constipation, taste disturbance and low appetite.

Risks were calculated from pooled risk differences.

High risk of bias. Only 11 of 16 trials reported on adverse events, suggesting a selective reporting bias.

Serious inconsistency. Numerous probiotic agents and doses were evaluated amongst a relatively small number of trials, limiting our ability to draw conclusions on the safety of the many probiotics agents and doses administered.

Serious unexplained inconsistency (large heterogeneity I = 79%, P value [P = 0.04], point estimates and confidence intervals vary considerably).

Serious imprecision. The upper bound of 0.02 fewer days of diarrhoea is not considered patient important.

Serious unexplained inconsistency (large heterogeneity I = 78%, P value [P = 0.05], point estimates and confidence intervals vary considerably).

Serious imprecision. The 95% confidence interval includes no effect and lower bound of 0.60 stools per day is of questionable patient importance.

14.1.4 Producing ‘Summary of findings’ tables

The GRADE Working Group’s software, GRADEpro GDT ( www.gradepro.org ), including GRADE’s interactive handbook, is available to assist review authors in the preparation of ‘Summary of findings’ tables. GRADEpro can use data on the comparator group risk and the effect estimate (entered by the review authors or imported from files generated in RevMan) to produce the relative effects and absolute risks associated with experimental interventions. In addition, it leads the user through the process of a GRADE assessment, and produces a table that can be used as a standalone table in a review (including by direct import into software such as RevMan or integration with RevMan Web), or an interactive ‘Summary of findings’ table (see help resources in GRADEpro).

14.1.5 Statistical considerations in ‘Summary of findings’ tables

14.1.5.1 dichotomous outcomes.

‘Summary of findings’ tables should include both absolute and relative measures of effect for dichotomous outcomes. Risk ratios, odds ratios and risk differences are different ways of comparing two groups with dichotomous outcome data (see Chapter 6, Section 6.4.1 ). Furthermore, there are two distinct risk ratios, depending on which event (e.g. ‘yes’ or ‘no’) is the focus of the analysis (see Chapter 6, Section 6.4.1.5 ). In the presence of a non-zero intervention effect, any variation across studies in the comparator group risks (i.e. variation in the risk of the event occurring without the intervention of interest, for example in different populations) makes it impossible for more than one of these measures to be truly the same in every study.

It has long been assumed in epidemiology that relative measures of effect are more consistent than absolute measures of effect from one scenario to another. There is empirical evidence to support this assumption (Engels et al 2000, Deeks and Altman 2001, Furukawa et al 2002). For this reason, meta-analyses should generally use either a risk ratio or an odds ratio as a measure of effect (see Chapter 10, Section 10.4.3 ). Correspondingly, a single estimate of relative effect is likely to be a more appropriate summary than a single estimate of absolute effect. If a relative effect is indeed consistent across studies, then different comparator group risks will have different implications for absolute benefit. For instance, if the risk ratio is consistently 0.75, then the experimental intervention would reduce a comparator group risk of 80% to 60% in the intervention group (an absolute risk reduction of 20 percentage points), but would also reduce a comparator group risk of 20% to 15% in the intervention group (an absolute risk reduction of 5 percentage points).

‘Summary of findings’ tables are built around the assumption of a consistent relative effect. It is therefore important to consider the implications of this effect for different comparator group risks (these can be derived or estimated from a number of sources, see Section 14.1.6.3 ), which may require an assessment of the certainty of evidence for prognostic evidence (Spencer et al 2012, Iorio et al 2015). For any comparator group risk, it is possible to estimate a corresponding intervention group risk (i.e. the absolute risk with the intervention) from the meta-analytic risk ratio or odds ratio. Note that the numbers provided in the ‘Corresponding risk’ column are specific to the ‘risks’ in the adjacent column.

For the meta-analytic risk ratio (RR) and assumed comparator risk (ACR) the corresponding intervention risk is obtained as:

summary of findings in quantitative research

As an example, in Figure 14.1.a , the meta-analytic risk ratio for symptomless deep vein thrombosis (DVT) is RR = 0.10 (95% CI 0.04 to 0.26). Assuming a comparator risk of ACR = 10 per 1000 = 0.01, we obtain:

summary of findings in quantitative research

For the meta-analytic odds ratio (OR) and assumed comparator risk, ACR, the corresponding intervention risk is obtained as:

summary of findings in quantitative research

Upper and lower confidence limits for the corresponding intervention risk are obtained by replacing RR or OR by their upper and lower confidence limits, respectively (e.g. replacing 0.10 with 0.04, then with 0.26, in the example). Such confidence intervals do not incorporate uncertainty in the assumed comparator risks.

When dealing with risk ratios, it is critical that the same definition of ‘event’ is used as was used for the meta-analysis. For example, if the meta-analysis focused on ‘death’ (as opposed to survival) as the event, then corresponding risks in the ‘Summary of findings’ table must also refer to ‘death’.

In (rare) circumstances in which there is clear rationale to assume a consistent risk difference in the meta-analysis, in principle it is possible to present this for relevant ‘assumed risks’ and their corresponding risks, and to present the corresponding (different) relative effects for each assumed risk.

The risk difference expresses the difference between the ACR and the corresponding intervention risk (or the difference between the experimental and the comparator intervention).

For the meta-analytic risk ratio (RR) and assumed comparator risk (ACR) the corresponding risk difference is obtained as (note that risks can also be expressed using percentage or percentage points):

summary of findings in quantitative research

As an example, in Figure 14.1.b the meta-analytic risk ratio is 0.41 (95% CI 0.29 to 0.55) for diarrhoea in children less than 5 years of age. Assuming a comparator group risk of 22.3% we obtain:

summary of findings in quantitative research

For the meta-analytic odds ratio (OR) and assumed comparator risk (ACR) the absolute risk difference is obtained as (percentage points):

summary of findings in quantitative research

Upper and lower confidence limits for the absolute risk difference are obtained by re-running the calculation above while replacing RR or OR by their upper and lower confidence limits, respectively (e.g. replacing 0.41 with 0.28, then with 0.55, in the example). Such confidence intervals do not incorporate uncertainty in the assumed comparator risks.

14.1.5.2 Time-to-event outcomes

Time-to-event outcomes measure whether and when a particular event (e.g. death) occurs (van Dalen et al 2007). The impact of the experimental intervention relative to the comparison group on time-to-event outcomes is usually measured using a hazard ratio (HR) (see Chapter 6, Section 6.8.1 ).

A hazard ratio expresses a relative effect estimate. It may be used in various ways to obtain absolute risks and other interpretable quantities for a specific population. Here we describe how to re-express hazard ratios in terms of: (i) absolute risk of event-free survival within a particular period of time; (ii) absolute risk of an event within a particular period of time; and (iii) median time to the event. All methods are built on an assumption of consistent relative effects (i.e. that the hazard ratio does not vary over time).

(i) Absolute risk of event-free survival within a particular period of time Event-free survival (e.g. overall survival) is commonly reported by individual studies. To obtain absolute effects for time-to-event outcomes measured as event-free survival, the summary HR can be used in conjunction with an assumed proportion of patients who are event-free in the comparator group (Tierney et al 2007). This proportion of patients will be specific to a period of time of observation. However, it is not strictly necessary to specify this period of time. For instance, a proportion of 50% of event-free patients might apply to patients with a high event rate observed over 1 year, or to patients with a low event rate observed over 2 years.

summary of findings in quantitative research

As an example, suppose the meta-analytic hazard ratio is 0.42 (95% CI 0.25 to 0.72). Assuming a comparator group risk of event-free survival (e.g. for overall survival people being alive) at 2 years of ACR = 900 per 1000 = 0.9 we obtain:

summary of findings in quantitative research

so that that 956 per 1000 people will be alive with the experimental intervention at 2 years. The derivation of the risk should be explained in a comment or footnote.

(ii) Absolute risk of an event within a particular period of time To obtain this absolute effect, again the summary HR can be used (Tierney et al 2007):

summary of findings in quantitative research

In the example, suppose we assume a comparator group risk of events (e.g. for mortality, people being dead) at 2 years of ACR = 100 per 1000 = 0.1. We obtain:

summary of findings in quantitative research

so that that 44 per 1000 people will be dead with the experimental intervention at 2 years.

(iii) Median time to the event Instead of absolute numbers, the time to the event in the intervention and comparison groups can be expressed as median survival time in months or years. To obtain median survival time the pooled HR can be applied to an assumed median survival time in the comparator group (Tierney et al 2007):

summary of findings in quantitative research

In the example, assuming a comparator group median survival time of 80 months, we obtain:

summary of findings in quantitative research

For all three of these options for re-expressing results of time-to-event analyses, upper and lower confidence limits for the corresponding intervention risk are obtained by replacing HR by its upper and lower confidence limits, respectively (e.g. replacing 0.42 with 0.25, then with 0.72, in the example). Again, as for dichotomous outcomes, such confidence intervals do not incorporate uncertainty in the assumed comparator group risks. This is of special concern for long-term survival with a low or moderate mortality rate and a corresponding high number of censored patients (i.e. a low number of patients under risk and a high censoring rate).

14.1.6 Detailed contents of a ‘Summary of findings’ table

14.1.6.1 table title and header.

The title of each ‘Summary of findings’ table should specify the healthcare question, framed in terms of the population and making it clear exactly what comparison of interventions are made. In Figure 14.1.a , the population is people taking long aeroplane flights, the intervention is compression stockings, and the control is no compression stockings.

The first rows of each ‘Summary of findings’ table should provide the following ‘header’ information:

Patients or population This further clarifies the population (and possibly the subpopulations) of interest and ideally the magnitude of risk of the most crucial adverse outcome at which an intervention is directed. For instance, people on a long-haul flight may be at different risks for DVT; those using selective serotonin reuptake inhibitors (SSRIs) might be at different risk for side effects; while those with atrial fibrillation may be at low (< 1%), moderate (1% to 4%) or high (> 4%) yearly risk of stroke.

Setting This should state any specific characteristics of the settings of the healthcare question that might limit the applicability of the summary of findings to other settings (e.g. primary care in Europe and North America).

Intervention The experimental intervention.

Comparison The comparator intervention (including no specific intervention).

14.1.6.2 Outcomes

The rows of a ‘Summary of findings’ table should include all desirable and undesirable health outcomes (listed in order of importance) that are essential for decision making, up to a maximum of seven outcomes. If there are more outcomes in the review, review authors will need to omit the less important outcomes from the table, and the decision selecting which outcomes are critical or important to the review should be made during protocol development (see Chapter 3 ). Review authors should provide time frames for the measurement of the outcomes (e.g. 90 days or 12 months) and the type of instrument scores (e.g. ranging from 0 to 100).

Note that review authors should include the pre-specified critical and important outcomes in the table whether data are available or not. However, they should be alert to the possibility that the importance of an outcome (e.g. a serious adverse effect) may only become known after the protocol was written or the analysis was carried out, and should take appropriate actions to include these in the ‘Summary of findings’ table.

The ‘Summary of findings’ table can include effects in subgroups of the population for different comparator risks and effect sizes separately. For instance, in Figure 14.1.b effects are presented for children younger and older than 5 years separately. Review authors may also opt to produce separate ‘Summary of findings’ tables for different populations.

Review authors should include serious adverse events, but it might be possible to combine minor adverse events as a single outcome, and describe this in an explanatory footnote (note that it is not appropriate to add events together unless they are independent, that is, a participant who has experienced one adverse event has an unaffected chance of experiencing the other adverse event).

Outcomes measured at multiple time points represent a particular problem. In general, to keep the table simple, review authors should present multiple time points only for outcomes critical to decision making, where either the result or the decision made are likely to vary over time. The remainder should be presented at a common time point where possible.

Review authors can present continuous outcome measures in the ‘Summary of findings’ table and should endeavour to make these interpretable to the target audience. This requires that the units are clear and readily interpretable, for example, days of pain, or frequency of headache, and the name and scale of any measurement tools used should be stated (e.g. a Visual Analogue Scale, ranging from 0 to 100). However, many measurement instruments are not readily interpretable by non-specialist clinicians or patients, for example, points on a Beck Depression Inventory or quality of life score. For these, a more interpretable presentation might involve converting a continuous to a dichotomous outcome, such as >50% improvement (see Chapter 15, Section 15.5 ).

14.1.6.3 Best estimate of risk with comparator intervention

Review authors should provide up to three typical risks for participants receiving the comparator intervention. For dichotomous outcomes, we recommend that these be presented in the form of the number of people experiencing the event per 100 or 1000 people (natural frequency) depending on the frequency of the outcome. For continuous outcomes, this would be stated as a mean or median value of the outcome measured.

Estimated or assumed comparator intervention risks could be based on assessments of typical risks in different patient groups derived from the review itself, individual representative studies in the review, or risks derived from a systematic review of prognosis studies or other sources of evidence which may in turn require an assessment of the certainty for the prognostic evidence (Spencer et al 2012, Iorio et al 2015). Ideally, risks would reflect groups that clinicians can easily identify on the basis of their presenting features.

An explanatory footnote should specify the source or rationale for each comparator group risk, including the time period to which it corresponds where appropriate. In Figure 14.1.a , clinicians can easily differentiate individuals with risk factors for deep venous thrombosis from those without. If there is known to be little variation in baseline risk then review authors may use the median comparator group risk across studies. If typical risks are not known, an option is to choose the risk from the included studies, providing the second highest for a high and the second lowest for a low risk population.

14.1.6.4 Risk with intervention

For dichotomous outcomes, review authors should provide a corresponding absolute risk for each comparator group risk, along with a confidence interval. This absolute risk with the (experimental) intervention will usually be derived from the meta-analysis result presented in the relative effect column (see Section 14.1.6.6 ). Formulae are provided in Section 14.1.5 . Review authors should present the absolute effect in the same format as the risks with comparator intervention (see Section 14.1.6.3 ), for example as the number of people experiencing the event per 1000 people.

For continuous outcomes, a difference in means or standardized difference in means should be presented with its confidence interval. These will typically be obtained directly from a meta-analysis. Explanatory text should be used to clarify the meaning, as in Figures 14.1.a and 14.1.b .

14.1.6.5 Risk difference

For dichotomous outcomes, the risk difference can be provided using one of the ‘Summary of findings’ table formats as an additional option (see Figure 14.1.b ). This risk difference expresses the difference between the experimental and comparator intervention and will usually be derived from the meta-analysis result presented in the relative effect column (see Section 14.1.6.6 ). Formulae are provided in Section 14.1.5 . Review authors should present the risk difference in the same format as assumed and corresponding risks with comparator intervention (see Section 14.1.6.3 ); for example, as the number of people experiencing the event per 1000 people or as percentage points if the assumed and corresponding risks are expressed in percentage.

For continuous outcomes, if the ‘Summary of findings’ table includes this option, the mean difference can be presented here and the ‘corresponding risk’ column left blank (see Figure 14.1.b ).

14.1.6.6 Relative effect (95% CI)

The relative effect will typically be a risk ratio or odds ratio (or occasionally a hazard ratio) with its accompanying 95% confidence interval, obtained from a meta-analysis performed on the basis of the same effect measure. Risk ratios and odds ratios are similar when the comparator intervention risks are low and effects are small, but may differ considerably when comparator group risks increase. The meta-analysis may involve an assumption of either fixed or random effects, depending on what the review authors consider appropriate, and implying that the relative effect is either an estimate of the effect of the intervention, or an estimate of the average effect of the intervention across studies, respectively.

14.1.6.7 Number of participants (studies)

This column should include the number of participants assessed in the included studies for each outcome and the corresponding number of studies that contributed these participants.

14.1.6.8 Certainty of the evidence (GRADE)

Review authors should comment on the certainty of the evidence (also known as quality of the body of evidence or confidence in the effect estimates). Review authors should use the specific evidence grading system developed by the GRADE Working Group (Atkins et al 2004, Guyatt et al 2008, Guyatt et al 2011a), which is described in detail in Section 14.2 . The GRADE approach categorizes the certainty in a body of evidence as ‘high’, ‘moderate’, ‘low’ or ‘very low’ by outcome. This is a result of judgement, but the judgement process operates within a transparent structure. As an example, the certainty would be ‘high’ if the summary were of several randomized trials with low risk of bias, but the rating of certainty becomes lower if there are concerns about risk of bias, inconsistency, indirectness, imprecision or publication bias. Judgements other than of ‘high’ certainty should be made transparent using explanatory footnotes or the ‘Comments’ column in the ‘Summary of findings’ table (see Section 14.1.6.10 ).

14.1.6.9 Comments

The aim of the ‘Comments’ field is to help interpret the information or data identified in the row. For example, this may be on the validity of the outcome measure or the presence of variables that are associated with the magnitude of effect. Important caveats about the results should be flagged here. Not all rows will need comments, and it is best to leave a blank if there is nothing warranting a comment.

14.1.6.10 Explanations

Detailed explanations should be included as footnotes to support the judgements in the ‘Summary of findings’ table, such as the overall GRADE assessment. The explanations should describe the rationale for important aspects of the content. Table 14.1.a lists guidance for useful explanations. Explanations should be concise, informative, relevant, easy to understand and accurate. If explanations cannot be sufficiently described in footnotes, review authors should provide further details of the issues in the Results and Discussion sections of the review.

Table 14.1.a Guidance for providing useful explanations in ‘Summary of findings’ (SoF) tables. Adapted from Santesso et al (2016)

, Chi , Tau), or the overlap of confidence intervals, or similarity of point estimates. , describe it as considerable, substantial, moderate or not important.

14.2 Assessing the certainty or quality of a body of evidence

14.2.1 the grade approach.

The Grades of Recommendation, Assessment, Development and Evaluation Working Group (GRADE Working Group) has developed a system for grading the certainty of evidence (Schünemann et al 2003, Atkins et al 2004, Schünemann et al 2006, Guyatt et al 2008, Guyatt et al 2011a). Over 100 organizations including the World Health Organization (WHO), the American College of Physicians, the American Society of Hematology (ASH), the Canadian Agency for Drugs and Technology in Health (CADTH) and the National Institutes of Health and Clinical Excellence (NICE) in the UK have adopted the GRADE system ( www.gradeworkinggroup.org ).

Cochrane has also formally adopted this approach, and all Cochrane Reviews should use GRADE to evaluate the certainty of evidence for important outcomes (see MECIR Box 14.2.a ).

MECIR Box 14.2.a Relevant expectations for conduct of intervention reviews

Assessing the certainty of the body of evidence ( )

GRADE is the most widely used approach for summarizing confidence in effects of interventions by outcome across studies. It is preferable to use the online GRADEpro tool, and to use it as described in the help system of the software. This should help to ensure that author teams are accessing the same information to inform their judgements. Ideally, two people working independently should assess the certainty of the body of evidence and reach a consensus view on any downgrading decisions. The five GRADE considerations should be addressed irrespective of whether the review includes a ‘Summary of findings’ table. It is helpful to draw on this information in the Discussion, in the Authors’ conclusions and to convey the certainty in the evidence in the Abstract and Plain language summary.

Justifying assessments of the certainty of the body of evidence ( )

The adoption of a structured approach ensures transparency in formulating an interpretation of the evidence, and the result is more informative to the user.

For systematic reviews, the GRADE approach defines the certainty of a body of evidence as the extent to which one can be confident that an estimate of effect or association is close to the quantity of specific interest. Assessing the certainty of a body of evidence involves consideration of within- and across-study risk of bias (limitations in study design and execution or methodological quality), inconsistency (or heterogeneity), indirectness of evidence, imprecision of the effect estimates and risk of publication bias (see Section 14.2.2 ), as well as domains that may increase our confidence in the effect estimate (as described in Section 14.2.3 ). The GRADE system entails an assessment of the certainty of a body of evidence for each individual outcome. Judgements about the domains that determine the certainty of evidence should be described in the results or discussion section and as part of the ‘Summary of findings’ table.

The GRADE approach specifies four levels of certainty ( Figure 14.2.a ). For interventions, including diagnostic and other tests that are evaluated as interventions (Schünemann et al 2008b, Schünemann et al 2008a, Balshem et al 2011, Schünemann et al 2012), the starting point for rating the certainty of evidence is categorized into two types:

  • randomized trials; and
  • non-randomized studies of interventions (NRSI), including observational studies (including but not limited to cohort studies, and case-control studies, cross-sectional studies, case series and case reports, although not all of these designs are usually included in Cochrane Reviews).

There are many instances in which review authors rely on information from NRSI, in particular to evaluate potential harms (see Chapter 24 ). In addition, review authors can obtain relevant data from both randomized trials and NRSI, with each type of evidence complementing the other (Schünemann et al 2013).

In GRADE, a body of evidence from randomized trials begins with a high-certainty rating while a body of evidence from NRSI begins with a low-certainty rating. The lower rating with NRSI is the result of the potential bias induced by the lack of randomization (i.e. confounding and selection bias).

However, when using the new Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool (Sterne et al 2016), an assessment tool that covers the risk of bias due to lack of randomization, all studies may start as high certainty of the evidence (Schünemann et al 2018). The approach of starting all study designs (including NRSI) as high certainty does not conflict with the initial GRADE approach of starting the rating of NRSI as low certainty evidence. This is because a body of evidence from NRSI should generally be downgraded by two levels due to the inherent risk of bias associated with the lack of randomization, namely confounding and selection bias. Not downgrading NRSI from high to low certainty needs transparent and detailed justification for what mitigates concerns about confounding and selection bias (Schünemann et al 2018). Very few examples of where not rating down by two levels is appropriate currently exist.

The highest certainty rating is a body of evidence when there are no concerns in any of the GRADE factors listed in Figure 14.2.a . Review authors often downgrade evidence to moderate, low or even very low certainty evidence, depending on the presence of the five factors in Figure 14.2.a . Usually, certainty rating will fall by one level for each factor, up to a maximum of three levels for all factors. If there are very severe problems for any one domain (e.g. when assessing risk of bias, all studies were unconcealed, unblinded and lost over 50% of their patients to follow-up), evidence may fall by two levels due to that factor alone. It is not possible to rate lower than ‘very low certainty’ evidence.

Review authors will generally grade evidence from sound non-randomized studies as low certainty, even if ROBINS-I is used. If, however, such studies yield large effects and there is no obvious bias explaining those effects, review authors may rate the evidence as moderate or – if the effect is large enough – even as high certainty ( Figure 14.2.a ). The very low certainty level is appropriate for, but is not limited to, studies with critical problems and unsystematic clinical observations (e.g. case series or case reports).

Figure 14.2.a Levels of the certainty of a body of evidence in the GRADE approach. *Upgrading criteria are usually applicable to non-randomized studies only (but exceptions exist).


 


 


 

 

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14.2.2 Domains that can lead to decreasing the certainty level of a body of evidence   

We now describe in more detail the five reasons (or domains) for downgrading the certainty of a body of evidence for a specific outcome. In each case, if no reason is found for downgrading the evidence, it should be classified as 'no limitation or not serious' (not important enough to warrant downgrading). If a reason is found for downgrading the evidence, it should be classified as 'serious' (downgrading the certainty rating by one level) or 'very serious' (downgrading the certainty grade by two levels). For non-randomized studies assessed with ROBINS-I, rating down by three levels should be classified as 'extremely' serious.

(1) Risk of bias or limitations in the detailed design and implementation

Our confidence in an estimate of effect decreases if studies suffer from major limitations that are likely to result in a biased assessment of the intervention effect. For randomized trials, these methodological limitations include failure to generate a random sequence, lack of allocation sequence concealment, lack of blinding (particularly with subjective outcomes that are highly susceptible to biased assessment), a large loss to follow-up or selective reporting of outcomes. Chapter 8 provides a discussion of study-level assessments of risk of bias in the context of a Cochrane Review, and proposes an approach to assessing the risk of bias for an outcome across studies as ‘Low’ risk of bias, ‘Some concerns’ and ‘High’ risk of bias for randomized trials. Levels of ‘Low’. ‘Moderate’, ‘Serious’ and ‘Critical’ risk of bias arise for non-randomized studies assessed with ROBINS-I ( Chapter 25 ). These assessments should feed directly into this GRADE domain. In particular, ‘Low’ risk of bias would indicate ‘no limitation’; ‘Some concerns’ would indicate either ‘no limitation’ or ‘serious limitation’; and ‘High’ risk of bias would indicate either ‘serious limitation’ or ‘very serious limitation’. ‘Critical’ risk of bias on ROBINS-I would indicate extremely serious limitations in GRADE. Review authors should use their judgement to decide between alternative categories, depending on the likely magnitude of the potential biases.

Every study addressing a particular outcome will differ, to some degree, in the risk of bias. Review authors should make an overall judgement on whether the certainty of evidence for an outcome warrants downgrading on the basis of study limitations. The assessment of study limitations should apply to the studies contributing to the results in the ‘Summary of findings’ table, rather than to all studies that could potentially be included in the analysis. We have argued in Chapter 7, Section 7.6.2 , that the primary analysis should be restricted to studies at low (or low and unclear) risk of bias where possible.

Table 14.2.a presents the judgements that must be made in going from assessments of the risk of bias to judgements about study limitations for each outcome included in a ‘Summary of findings’ table. A rating of high certainty evidence can be achieved only when most evidence comes from studies that met the criteria for low risk of bias. For example, of the 22 studies addressing the impact of beta-blockers on mortality in patients with heart failure, most probably or certainly used concealed allocation of the sequence, all blinded at least some key groups and follow-up of randomized patients was almost complete (Brophy et al 2001). The certainty of evidence might be downgraded by one level when most of the evidence comes from individual studies either with a crucial limitation for one item, or with some limitations for multiple items. An example of very serious limitations, warranting downgrading by two levels, is provided by evidence on surgery versus conservative treatment in the management of patients with lumbar disc prolapse (Gibson and Waddell 2007). We are uncertain of the benefit of surgery in reducing symptoms after one year or longer, because the one study included in the analysis had inadequate concealment of the allocation sequence and the outcome was assessed using a crude rating by the surgeon without blinding.

(2) Unexplained heterogeneity or inconsistency of results

When studies yield widely differing estimates of effect (heterogeneity or variability in results), investigators should look for robust explanations for that heterogeneity. For instance, drugs may have larger relative effects in sicker populations or when given in larger doses. A detailed discussion of heterogeneity and its investigation is provided in Chapter 10, Section 10.10 and Section 10.11 . If an important modifier exists, with good evidence that important outcomes are different in different subgroups (which would ideally be pre-specified), then a separate ‘Summary of findings’ table may be considered for a separate population. For instance, a separate ‘Summary of findings’ table would be used for carotid endarterectomy in symptomatic patients with high grade stenosis (70% to 99%) in which the intervention is, in the hands of the right surgeons, beneficial, and another (if review authors considered it relevant) for asymptomatic patients with low grade stenosis (less than 30%) in which surgery appears harmful (Orrapin and Rerkasem 2017). When heterogeneity exists and affects the interpretation of results, but review authors are unable to identify a plausible explanation with the data available, the certainty of the evidence decreases.

(3) Indirectness of evidence

Two types of indirectness are relevant. First, a review comparing the effectiveness of alternative interventions (say A and B) may find that randomized trials are available, but they have compared A with placebo and B with placebo. Thus, the evidence is restricted to indirect comparisons between A and B. Where indirect comparisons are undertaken within a network meta-analysis context, GRADE for network meta-analysis should be used (see Chapter 11, Section 11.5 ).

Second, a review may find randomized trials that meet eligibility criteria but address a restricted version of the main review question in terms of population, intervention, comparator or outcomes. For example, suppose that in a review addressing an intervention for secondary prevention of coronary heart disease, most identified studies happened to be in people who also had diabetes. Then the evidence may be regarded as indirect in relation to the broader question of interest because the population is primarily related to people with diabetes. The opposite scenario can equally apply: a review addressing the effect of a preventive strategy for coronary heart disease in people with diabetes may consider studies in people without diabetes to provide relevant, albeit indirect, evidence. This would be particularly likely if investigators had conducted few if any randomized trials in the target population (e.g. people with diabetes). Other sources of indirectness may arise from interventions studied (e.g. if in all included studies a technical intervention was implemented by expert, highly trained specialists in specialist centres, then evidence on the effects of the intervention outside these centres may be indirect), comparators used (e.g. if the comparator groups received an intervention that is less effective than standard treatment in most settings) and outcomes assessed (e.g. indirectness due to surrogate outcomes when data on patient-important outcomes are not available, or when investigators seek data on quality of life but only symptoms are reported). Review authors should make judgements transparent when they believe downgrading is justified, based on differences in anticipated effects in the group of primary interest. Review authors may be aided and increase transparency of their judgements about indirectness if they use Table 14.2.b available in the GRADEpro GDT software (Schünemann et al 2013).

(4) Imprecision of results

When studies include few participants or few events, and thus have wide confidence intervals, review authors can lower their rating of the certainty of the evidence. The confidence intervals included in the ‘Summary of findings’ table will provide readers with information that allows them to make, to some extent, their own rating of precision. Review authors can use a calculation of the optimal information size (OIS) or review information size (RIS), similar to sample size calculations, to make judgements about imprecision (Guyatt et al 2011b, Schünemann 2016). The OIS or RIS is calculated on the basis of the number of participants required for an adequately powered individual study. If the 95% confidence interval excludes a risk ratio (RR) of 1.0, and the total number of events or patients exceeds the OIS criterion, precision is adequate. If the 95% CI includes appreciable benefit or harm (an RR of under 0.75 or over 1.25 is often suggested as a very rough guide) downgrading for imprecision may be appropriate even if OIS criteria are met (Guyatt et al 2011b, Schünemann 2016).

(5) High probability of publication bias

The certainty of evidence level may be downgraded if investigators fail to report studies on the basis of results (typically those that show no effect: publication bias) or outcomes (typically those that may be harmful or for which no effect was observed: selective outcome non-reporting bias). Selective reporting of outcomes from among multiple outcomes measured is assessed at the study level as part of the assessment of risk of bias (see Chapter 8, Section 8.7 ), so for the studies contributing to the outcome in the ‘Summary of findings’ table this is addressed by domain 1 above (limitations in the design and implementation). If a large number of studies included in the review do not contribute to an outcome, or if there is evidence of publication bias, the certainty of the evidence may be downgraded. Chapter 13 provides a detailed discussion of reporting biases, including publication bias, and how it may be tackled in a Cochrane Review. A prototypical situation that may elicit suspicion of publication bias is when published evidence includes a number of small studies, all of which are industry-funded (Bhandari et al 2004). For example, 14 studies of flavanoids in patients with haemorrhoids have shown apparent large benefits, but enrolled a total of only 1432 patients (i.e. each study enrolled relatively few patients) (Alonso-Coello et al 2006). The heavy involvement of sponsors in most of these studies raises questions of whether unpublished studies that suggest no benefit exist (publication bias).

A particular body of evidence can suffer from problems associated with more than one of the five factors listed here, and the greater the problems, the lower the certainty of evidence rating that should result. One could imagine a situation in which randomized trials were available, but all or virtually all of these limitations would be present, and in serious form. A very low certainty of evidence rating would result.

Table 14.2.a Further guidelines for domain 1 (of 5) in a GRADE assessment: going from assessments of risk of bias in studies to judgements about study limitations for main outcomes across studies

Low risk of bias

Most information is from results at low risk of bias.

Plausible bias unlikely to seriously alter the results.

No apparent limitations.

No serious limitations, do not downgrade.

Some concerns

Most information is from results at low risk of bias or with some concerns.

Plausible bias that raises some doubt about the results.

Potential limitations are unlikely to lower confidence in the estimate of effect.

No serious limitations, do not downgrade.

Potential limitations are likely to lower confidence in the estimate of effect.

Serious limitations, downgrade one level.

High risk of bias

The proportion of information from results at high risk of bias is sufficient to affect the interpretation of results.

Plausible bias that seriously weakens confidence in the results.

Crucial limitation for one criterion, or some limitations for multiple criteria, sufficient to lower confidence in the estimate of effect.

Serious limitations, downgrade one level.

Crucial limitation for one or more criteria sufficient to substantially lower confidence in the estimate of effect.

Very serious limitations, downgrade two levels.

Table 14.2.b Judgements about indirectness by outcome (available in GRADEpro GDT)

 

Probably yes

Probably no

No

 

 

 

 

Intervention:

Yes

Probably yes

Probably no

No

 

 

 

 

Comparator:

Direct comparison:

Final judgement about indirectness across domains:

 

14.2.3 Domains that may lead to increasing the certainty level of a body of evidence

Although NRSI and downgraded randomized trials will generally yield a low rating for certainty of evidence, there will be unusual circumstances in which review authors could ‘upgrade’ such evidence to moderate or even high certainty ( Table 14.3.a ).

  • Large effects On rare occasions when methodologically well-done observational studies yield large, consistent and precise estimates of the magnitude of an intervention effect, one may be particularly confident in the results. A large estimated effect (e.g. RR >2 or RR <0.5) in the absence of plausible confounders, or a very large effect (e.g. RR >5 or RR <0.2) in studies with no major threats to validity, might qualify for this. In these situations, while the NRSI may possibly have provided an over-estimate of the true effect, the weak study design may not explain all of the apparent observed benefit. Thus, despite reservations based on the observational study design, review authors are confident that the effect exists. The magnitude of the effect in these studies may move the assigned certainty of evidence from low to moderate (if the effect is large in the absence of other methodological limitations). For example, a meta-analysis of observational studies showed that bicycle helmets reduce the risk of head injuries in cyclists by a large margin (odds ratio (OR) 0.31, 95% CI 0.26 to 0.37) (Thompson et al 2000). This large effect, in the absence of obvious bias that could create the association, suggests a rating of moderate-certainty evidence.  Note : GRADE guidance suggests the possibility of rating up one level for a large effect if the relative effect is greater than 2.0. However, if the point estimate of the relative effect is greater than 2.0, but the confidence interval is appreciably below 2.0, then some hesitation would be appropriate in the decision to rate up for a large effect. Another situation allows inference of a strong association without a formal comparative study. Consider the question of the impact of routine colonoscopy versus no screening for colon cancer on the rate of perforation associated with colonoscopy. Here, a large series of representative patients undergoing colonoscopy may provide high certainty evidence about the risk of perforation associated with colonoscopy. When the risk of the event among patients receiving the relevant comparator is known to be near 0 (i.e. we are certain that the incidence of spontaneous colon perforation in patients not undergoing colonoscopy is extremely low), case series or cohort studies of representative patients can provide high certainty evidence of adverse effects associated with an intervention, thereby allowing us to infer a strong association from even a limited number of events.
  • Dose-response The presence of a dose-response gradient may increase our confidence in the findings of observational studies and thereby enhance the assigned certainty of evidence. For example, our confidence in the result of observational studies that show an increased risk of bleeding in patients who have supratherapeutic anticoagulation levels is increased by the observation that there is a dose-response gradient between the length of time needed for blood to clot (as measured by the international normalized ratio (INR)) and an increased risk of bleeding (Levine et al 2004). A systematic review of NRSI investigating the effect of cyclooxygenase-2 inhibitors on cardiovascular events found that the summary estimate (RR) with rofecoxib was 1.33 (95% CI 1.00 to 1.79) with doses less than 25mg/d, and 2.19 (95% CI 1.64 to 2.91) with doses more than 25mg/d. Although residual confounding is likely to exist in the NRSI that address this issue, the existence of a dose-response gradient and the large apparent effect of higher doses of rofecoxib markedly increase our strength of inference that the association cannot be explained by residual confounding, and is therefore likely to be both causal and, at high levels of exposure, substantial.  Note : GRADE guidance suggests the possibility of rating up one level for a large effect if the relative effect is greater than 2.0. Here, the fact that the point estimate of the relative effect is greater than 2.0, but the confidence interval is appreciably below 2.0 might make some hesitate in the decision to rate up for a large effect
  • Plausible confounding On occasion, all plausible biases from randomized or non-randomized studies may be working to under-estimate an apparent intervention effect. For example, if only sicker patients receive an experimental intervention or exposure, yet they still fare better, it is likely that the actual intervention or exposure effect is larger than the data suggest. For instance, a rigorous systematic review of observational studies including a total of 38 million patients demonstrated higher death rates in private for-profit versus private not-for-profit hospitals (Devereaux et al 2002). One possible bias relates to different disease severity in patients in the two hospital types. It is likely, however, that patients in the not-for-profit hospitals were sicker than those in the for-profit hospitals. Thus, to the extent that residual confounding existed, it would bias results against the not-for-profit hospitals. The second likely bias was the possibility that higher numbers of patients with excellent private insurance coverage could lead to a hospital having more resources and a spill-over effect that would benefit those without such coverage. Since for-profit hospitals are likely to admit a larger proportion of such well-insured patients than not-for-profit hospitals, the bias is once again against the not-for-profit hospitals. Since the plausible biases would all diminish the demonstrated intervention effect, one might consider the evidence from these observational studies as moderate rather than low certainty. A parallel situation exists when observational studies have failed to demonstrate an association, but all plausible biases would have increased an intervention effect. This situation will usually arise in the exploration of apparent harmful effects. For example, because the hypoglycaemic drug phenformin causes lactic acidosis, the related agent metformin was under suspicion for the same toxicity. Nevertheless, very large observational studies have failed to demonstrate an association (Salpeter et al 2007). Given the likelihood that clinicians would be more alert to lactic acidosis in the presence of the agent and over-report its occurrence, one might consider this moderate, or even high certainty, evidence refuting a causal relationship between typical therapeutic doses of metformin and lactic acidosis.

14.3 Describing the assessment of the certainty of a body of evidence using the GRADE framework

Review authors should report the grading of the certainty of evidence in the Results section for each outcome for which this has been performed, providing the rationale for downgrading or upgrading the evidence, and referring to the ‘Summary of findings’ table where applicable.

Table 14.3.a provides a framework and examples for how review authors can justify their judgements about the certainty of evidence in each domain. These justifications should also be included in explanatory notes to the ‘Summary of Findings’ table (see Section 14.1.6.10 ).

Chapter 15, Section 15.6 , describes in more detail how the overall GRADE assessment across all domains can be used to draw conclusions about the effects of the intervention, as well as providing implications for future research.

Table 14.3.a Framework for describing the certainty of evidence and justifying downgrading or upgrading

Describe the risk of bias based on the criteria used in the risk-of-bias table.

Downgraded because of 10 randomized trials, five did not blind patients and caretakers.

Describe the degree of inconsistency by outcome using one or more indicators (e.g. I and P value), confidence interval overlap, difference in point estimate, between-study variance.

Not downgraded because the proportion of the variability in effect estimates that is due to true heterogeneity rather than chance is not important (I = 0%).

Describe if the majority of studies address the PICO – were they similar to the question posed?

Downgraded because the included studies were restricted to patients with advanced cancer.

Describe the number of events, and width of the confidence intervals.

The confidence intervals for the effect on mortality are consistent with both an appreciable benefit and appreciable harm and we lowered the certainty.

Describe the possible degree of publication bias.

1. The funnel plot of 14 randomized trials indicated that there were several small studies that showed a small positive effect, but small studies that showed no effect or harm may have been unpublished. The certainty of the evidence was lowered.

2. There are only three small positive studies, it appears that studies showing no effect or harm have not been published. There also is for-profit interest in the intervention. The certainty of the evidence was lowered.

Describe the magnitude of the effect and the widths of the associate confidence intervals.

Upgraded because the RR is large: 0.3 (95% CI 0.2 to 0.4), with a sufficient number of events to be precise.

 

The studies show a clear relation with increases in the outcome of an outcome (e.g. lung cancer) with higher exposure levels.

Upgraded because the dose-response relation shows a relative risk increase of 10% in never smokers, 15% in smokers of 10 pack years and 20% in smokers of 15 pack years.

Describe which opposing plausible biases and confounders may have not been considered.

The estimate of effect is not controlled for the following possible confounders: smoking, degree of education, but the distribution of these factors in the studies is likely to lead to an under-estimate of the true effect. The certainty of the evidence was increased.

14.4 Chapter information

Authors: Holger J Schünemann, Julian PT Higgins, Gunn E Vist, Paul Glasziou, Elie A Akl, Nicole Skoetz, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group (formerly Applicability and Recommendations Methods Group) and the Cochrane Statistical Methods Group

Acknowledgements: Andrew D Oxman contributed to earlier versions. Professor Penny Hawe contributed to the text on adverse effects in earlier versions. Jon Deeks provided helpful contributions on an earlier version of this chapter. For details of previous authors and editors of the Handbook , please refer to the Preface.

Funding: This work was in part supported by funding from the Michael G DeGroote Cochrane Canada Centre and the Ontario Ministry of Health.

14.5 References

Alonso-Coello P, Zhou Q, Martinez-Zapata MJ, Mills E, Heels-Ansdell D, Johanson JF, Guyatt G. Meta-analysis of flavonoids for the treatment of haemorrhoids. British Journal of Surgery 2006; 93 : 909-920.

Atkins D, Best D, Briss PA, Eccles M, Falck-Ytter Y, Flottorp S, Guyatt GH, Harbour RT, Haugh MC, Henry D, Hill S, Jaeschke R, Leng G, Liberati A, Magrini N, Mason J, Middleton P, Mrukowicz J, O'Connell D, Oxman AD, Phillips B, Schünemann HJ, Edejer TT, Varonen H, Vist GE, Williams JW, Jr., Zaza S. Grading quality of evidence and strength of recommendations. BMJ 2004; 328 : 1490.

Balshem H, Helfand M, Schünemann HJ, Oxman AD, Kunz R, Brozek J, Vist GE, Falck-Ytter Y, Meerpohl J, Norris S, Guyatt GH. GRADE guidelines: 3. Rating the quality of evidence. Journal of Clinical Epidemiology 2011; 64 : 401-406.

Bhandari M, Busse JW, Jackowski D, Montori VM, Schünemann H, Sprague S, Mears D, Schemitsch EH, Heels-Ansdell D, Devereaux PJ. Association between industry funding and statistically significant pro-industry findings in medical and surgical randomized trials. Canadian Medical Association Journal 2004; 170 : 477-480.

Brophy JM, Joseph L, Rouleau JL. Beta-blockers in congestive heart failure. A Bayesian meta-analysis. Annals of Internal Medicine 2001; 134 : 550-560.

Carrasco-Labra A, Brignardello-Petersen R, Santesso N, Neumann I, Mustafa RA, Mbuagbaw L, Etxeandia Ikobaltzeta I, De Stio C, McCullagh LJ, Alonso-Coello P, Meerpohl JJ, Vandvik PO, Brozek JL, Akl EA, Bossuyt P, Churchill R, Glenton C, Rosenbaum S, Tugwell P, Welch V, Garner P, Guyatt G, Schünemann HJ. Improving GRADE evidence tables part 1: a randomized trial shows improved understanding of content in summary of findings tables with a new format. Journal of Clinical Epidemiology 2016; 74 : 7-18.

Deeks JJ, Altman DG. Effect measures for meta-analysis of trials with binary outcomes. In: Egger M, Davey Smith G, Altman DG, editors. Systematic Reviews in Health Care: Meta-analysis in Context . 2nd ed. London (UK): BMJ Publication Group; 2001. p. 313-335.

Devereaux PJ, Choi PT, Lacchetti C, Weaver B, Schünemann HJ, Haines T, Lavis JN, Grant BJ, Haslam DR, Bhandari M, Sullivan T, Cook DJ, Walter SD, Meade M, Khan H, Bhatnagar N, Guyatt GH. A systematic review and meta-analysis of studies comparing mortality rates of private for-profit and private not-for-profit hospitals. Canadian Medical Association Journal 2002; 166 : 1399-1406.

Engels EA, Schmid CH, Terrin N, Olkin I, Lau J. Heterogeneity and statistical significance in meta-analysis: an empirical study of 125 meta-analyses. Statistics in Medicine 2000; 19 : 1707-1728.

Furukawa TA, Guyatt GH, Griffith LE. Can we individualize the 'number needed to treat'? An empirical study of summary effect measures in meta-analyses. International Journal of Epidemiology 2002; 31 : 72-76.

Gibson JN, Waddell G. Surgical interventions for lumbar disc prolapse: updated Cochrane Review. Spine 2007; 32 : 1735-1747.

Guyatt G, Oxman A, Vist G, Kunz R, Falck-Ytter Y, Alonso-Coello P, Schünemann H. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008; 336 : 3.

Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, Norris S, Falck-Ytter Y, Glasziou P, DeBeer H, Jaeschke R, Rind D, Meerpohl J, Dahm P, Schünemann HJ. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. Journal of Clinical Epidemiology 2011a; 64 : 383-394.

Guyatt GH, Oxman AD, Kunz R, Brozek J, Alonso-Coello P, Rind D, Devereaux PJ, Montori VM, Freyschuss B, Vist G, Jaeschke R, Williams JW, Jr., Murad MH, Sinclair D, Falck-Ytter Y, Meerpohl J, Whittington C, Thorlund K, Andrews J, Schünemann HJ. GRADE guidelines 6. Rating the quality of evidence--imprecision. Journal of Clinical Epidemiology 2011b; 64 : 1283-1293.

Iorio A, Spencer FA, Falavigna M, Alba C, Lang E, Burnand B, McGinn T, Hayden J, Williams K, Shea B, Wolff R, Kujpers T, Perel P, Vandvik PO, Glasziou P, Schünemann H, Guyatt G. Use of GRADE for assessment of evidence about prognosis: rating confidence in estimates of event rates in broad categories of patients. BMJ 2015; 350 : h870.

Langendam M, Carrasco-Labra A, Santesso N, Mustafa RA, Brignardello-Petersen R, Ventresca M, Heus P, Lasserson T, Moustgaard R, Brozek J, Schünemann HJ. Improving GRADE evidence tables part 2: a systematic survey of explanatory notes shows more guidance is needed. Journal of Clinical Epidemiology 2016; 74 : 19-27.

Levine MN, Raskob G, Landefeld S, Kearon C, Schulman S. Hemorrhagic complications of anticoagulant treatment: the Seventh ACCP Conference on Antithrombotic and Thrombolytic Therapy. Chest 2004; 126 : 287S-310S.

Orrapin S, Rerkasem K. Carotid endarterectomy for symptomatic carotid stenosis. Cochrane Database of Systematic Reviews 2017; 6 : CD001081.

Salpeter S, Greyber E, Pasternak G, Salpeter E. Risk of fatal and nonfatal lactic acidosis with metformin use in type 2 diabetes mellitus. Cochrane Database of Systematic Reviews 2007; 4 : CD002967.

Santesso N, Carrasco-Labra A, Langendam M, Brignardello-Petersen R, Mustafa RA, Heus P, Lasserson T, Opiyo N, Kunnamo I, Sinclair D, Garner P, Treweek S, Tovey D, Akl EA, Tugwell P, Brozek JL, Guyatt G, Schünemann HJ. Improving GRADE evidence tables part 3: detailed guidance for explanatory footnotes supports creating and understanding GRADE certainty in the evidence judgments. Journal of Clinical Epidemiology 2016; 74 : 28-39.

Schünemann HJ, Best D, Vist G, Oxman AD, Group GW. Letters, numbers, symbols and words: how to communicate grades of evidence and recommendations. Canadian Medical Association Journal 2003; 169 : 677-680.

Schünemann HJ, Jaeschke R, Cook DJ, Bria WF, El-Solh AA, Ernst A, Fahy BF, Gould MK, Horan KL, Krishnan JA, Manthous CA, Maurer JR, McNicholas WT, Oxman AD, Rubenfeld G, Turino GM, Guyatt G. An official ATS statement: grading the quality of evidence and strength of recommendations in ATS guidelines and recommendations. American Journal of Respiratory and Critical Care Medicine 2006; 174 : 605-614.

Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Jaeschke R, Vist GE, Williams JW, Jr., Kunz R, Craig J, Montori VM, Bossuyt P, Guyatt GH. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ 2008a; 336 : 1106-1110.

Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Bossuyt P, Chang S, Muti P, Jaeschke R, Guyatt GH. GRADE: assessing the quality of evidence for diagnostic recommendations. ACP Journal Club 2008b; 149 : 2.

Schünemann HJ, Mustafa R, Brozek J. [Diagnostic accuracy and linked evidence--testing the chain]. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen 2012; 106 : 153-160.

Schünemann HJ, Tugwell P, Reeves BC, Akl EA, Santesso N, Spencer FA, Shea B, Wells G, Helfand M. Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews on the effects of interventions. Research Synthesis Methods 2013; 4 : 49-62.

Schünemann HJ. Interpreting GRADE's levels of certainty or quality of the evidence: GRADE for statisticians, considering review information size or less emphasis on imprecision? Journal of Clinical Epidemiology 2016; 75 : 6-15.

Schünemann HJ, Cuello C, Akl EA, Mustafa RA, Meerpohl JJ, Thayer K, Morgan RL, Gartlehner G, Kunz R, Katikireddi SV, Sterne J, Higgins JPT, Guyatt G, Group GW. GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence. Journal of Clinical Epidemiology 2018.

Spencer-Bonilla G, Quinones AR, Montori VM, International Minimally Disruptive Medicine W. Assessing the Burden of Treatment. Journal of General Internal Medicine 2017; 32 : 1141-1145.

Spencer FA, Iorio A, You J, Murad MH, Schünemann HJ, Vandvik PO, Crowther MA, Pottie K, Lang ES, Meerpohl JJ, Falck-Ytter Y, Alonso-Coello P, Guyatt GH. Uncertainties in baseline risk estimates and confidence in treatment effects. BMJ 2012; 345 : e7401.

Sterne JAC, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 2016; 355 : i4919.

Thompson DC, Rivara FP, Thompson R. Helmets for preventing head and facial injuries in bicyclists. Cochrane Database of Systematic Reviews 2000; 2 : CD001855.

Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials 2007; 8 .

van Dalen EC, Tierney JF, Kremer LCM. Tips and tricks for understanding and using SR results. No. 7: time‐to‐event data. Evidence-Based Child Health 2007; 2 : 1089-1090.

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Methodology

  • What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

  • What is the demographic makeup of Singapore in 2020?
  • How has the average temperature changed globally over the last century?
  • Does environmental pollution affect the prevalence of honey bees?
  • Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

  • In descriptive research , you simply seek an overall summary of your study variables.
  • In correlational research , you investigate relationships between your study variables.
  • In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Quantitative research methods
Research method How to use Example
Control or manipulate an to measure its effect on a dependent variable. To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task. You compare self-ratings of procrastination behaviors between the groups after the intervention.
Ask questions of a group of people in-person, over-the-phone or online. You distribute with rating scales to first-year international college students to investigate their experiences of culture shock.
(Systematic) observation Identify a behavior or occurrence of interest and monitor it in its natural setting. To study college classroom participation, you sit in on classes to observe them, counting and recording the prevalence of active and passive behaviors by students from different backgrounds.
Secondary research Collect data that has been gathered for other purposes e.g., national surveys or historical records. To assess whether attitudes towards climate change have changed since the 1980s, you collect relevant questionnaire data from widely available .

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

  • Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

  • Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

  • Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

  • Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

  • Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

  • Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

  • Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

  • Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Chi square goodness of fit test
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Inclusion and exclusion criteria

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On June 11, 2024

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

  • Always report the findings of your research in the past tense.
  • The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
  • Avoid reporting results that are not relevant to your research questions or research hypothesis.

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research methodology

1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Mean N Std. Deviation Std. Error Mean
PRESCORE 29.00 217 7.65 .519
PSTSCORE 26.00 217 9.74 .661

Paired Samples Correlation

N Correlation Sig.
PRESCORE & PSTSCORE 217 .448 .000

Paired Samples Test

Paired Differences
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference t df Sig. (2-tailed)
Lower Upper
Pair 1 PRESCORE-PSTSCORE 2.50 9.31 .632 1.26 3.75 3.967 216 .000

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

The last question of the interview focused on the need for improvement in Thai ready-to-eat products and the industry at large, emphasizing the need for enhancement in the current products being offered in the market. When asked if there was any particular need for Thai ready-to-eat meals to be improved and how to improve them in case of ‘yes,’ the males replied mainly by saying that the current products need improvement in terms of the use of healthier raw materials and preservatives or additives. There was an agreement amongst all males concerning the need to improve the industry for ready-to-eat meals and the use of more healthy items to prepare such meals. The females were also of the opinion that the fast-food items needed to be improved in the sense that more healthy raw materials such as vegetable oil and unsaturated fats, including whole-wheat products, to overcome risks associated with trans fat leading to obesity and hypertension should be used for the production of RTE products. The frozen RTE meals and packaged snacks included many preservatives and chemical-based flavouring enhancers that harmed human health and needed to be reduced. The industry is said to be aware of this fact and should try to produce RTE products that benefit the community in terms of healthy consumption.

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What to Avoid in Dissertation Findings Chapter

  • Avoid using interpretive and subjective phrases and terms such as “confirms,” “reveals,” “suggests,” or “validates.” These terms are more suitable for the discussion chapter , where you will be expected to interpret the results in detail.
  • Only briefly explain findings in relation to the key themes, hypothesis, and research questions. You don’t want to write a detailed subjective explanation for any research questions at this stage.

The Do’s of Writing the Findings or Results Section

  • Ensure you are not presenting results from other research studies in your findings.
  • Observe whether or not your hypothesis is tested or research questions answered.
  • Illustrations and tables present data and are labelled to help your readers understand what they relate to.
  • Use software such as Excel, STATA, and SPSS to analyse results and important trends.

Essential Guidelines on How to Write Dissertation Findings

The dissertation findings chapter should provide the context for understanding the results. The research problem should be repeated, and the research goals should be stated briefly.

This approach helps to gain the reader’s attention toward the research problem. The first step towards writing the findings is identifying which results will be presented in this section.

The results relevant to the questions must be presented, considering whether the results support the hypothesis. You do not need to include every result in the findings section. The next step is ensuring the data can be appropriately organized and accurate.

You will need to have a basic idea about writing the findings of a dissertation because this will provide you with the knowledge to arrange the data chronologically.

Start each paragraph by writing about the most important results and concluding the section with the most negligible actual results.

A short paragraph can conclude the findings section, summarising the findings so readers will remember as they transition to the next chapter. This is essential if findings are unexpected or unfamiliar or impact the study.

Our writers can help you with all parts of your dissertation, including statistical analysis of your results . To obtain free non-binding quotes, please complete our online quote form here .

Be Impartial in your Writing

When crafting your findings, knowing how you will organize the work is important. The findings are the story that needs to be told in response to the research questions that have been answered.

Therefore, the story needs to be organized to make sense to you and the reader. The findings must be compelling and responsive to be linked to the research questions being answered.

Always ensure that the size and direction of any changes, including percentage change, can be mentioned in the section. The details of p values or confidence intervals and limits should be included.

The findings sections only have the relevant parts of the primary evidence mentioned. Still, it is a good practice to include all the primary evidence in an appendix that can be referred to later.

The results should always be written neutrally without speculation or implication. The statement of the results mustn’t have any form of evaluation or interpretation.

Negative results should be added in the findings section because they validate the results and provide high neutrality levels.

The length of the dissertation findings chapter is an important question that must be addressed. It should be noted that the length of the section is directly related to the total word count of your dissertation paper.

The writer should use their discretion in deciding the length of the findings section or refer to the dissertation handbook or structure guidelines.

It should neither belong nor be short nor concise and comprehensive to highlight the reader’s main findings.

Ethically, you should be confident in the findings and provide counter-evidence. Anything that does not have sufficient evidence should be discarded. The findings should respond to the problem presented and provide a solution to those questions.

Structure of the Findings Chapter

The chapter should use appropriate words and phrases to present the results to the readers. Logical sentences should be used, while paragraphs should be linked to produce cohesive work.

You must ensure all the significant results have been added in the section. Recheck after completing the section to ensure no mistakes have been made.

The structure of the findings section is something you may have to be sure of primarily because it will provide the basis for your research work and ensure that the discussions section can be written clearly and proficiently.

One way to arrange the results is to provide a brief synopsis and then explain the essential findings. However, there should be no speculation or explanation of the results, as this will be done in the discussion section.

Another way to arrange the section is to present and explain a result. This can be done for all the results while the section is concluded with an overall synopsis.

This is the preferred method when you are writing more extended dissertations. It can be helpful when multiple results are equally significant. A brief conclusion should be written to link all the results and transition to the discussion section.

Numerous data analysis dissertation examples are available on the Internet, which will help you improve your understanding of writing the dissertation’s findings.

Problems to Avoid When Writing Dissertation Findings

One of the problems to avoid while writing the dissertation findings is reporting background information or explaining the findings. This should be done in the introduction section .

You can always revise the introduction chapter based on the data you have collected if that seems an appropriate thing to do.

Raw data or intermediate calculations should not be added in the findings section. Always ask your professor if raw data needs to be included.

If the data is to be included, then use an appendix or a set of appendices referred to in the text of the findings chapter.

Do not use vague or non-specific phrases in the findings section. It is important to be factual and concise for the reader’s benefit.

The findings section presents the crucial data collected during the research process. It should be presented concisely and clearly to the reader. There should be no interpretation, speculation, or analysis of the data.

The significant results should be categorized systematically with the text used with charts, figures, and tables. Furthermore, avoiding using vague and non-specific words in this section is essential.

It is essential to label the tables and visual material properly. You should also check and proofread the section to avoid mistakes.

The dissertation findings chapter is a critical part of your overall dissertation paper. If you struggle with presenting your results and statistical analysis, our expert dissertation writers can help you get things right. Whether you need help with the entire dissertation paper or individual chapters, our dissertation experts can provide customized dissertation support .

FAQs About Findings of a Dissertation

How do i report quantitative findings.

The best way to present your quantitative findings is to structure them around the research hypothesis or research questions you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the qualitative research results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your research question or hypothesis should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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How To Write A Research Summary

Deeptanshu D

It’s a common perception that writing a research summary is a quick and easy task. After all, how hard can jotting down 300 words be? But when you consider the weight those 300 words carry, writing a research summary as a part of your dissertation, essay or compelling draft for your paper instantly becomes daunting task.

A research summary requires you to synthesize a complex research paper into an informative, self-explanatory snapshot. It needs to portray what your article contains. Thus, writing it often comes at the end of the task list.

Regardless of when you’re planning to write, it is no less of a challenge, particularly if you’re doing it for the first time. This blog will take you through everything you need to know about research summary so that you have an easier time with it.

How to write a research summary

What is a Research Summary?

A research summary is the part of your research paper that describes its findings to the audience in a brief yet concise manner. A well-curated research summary represents you and your knowledge about the information written in the research paper.

While writing a quality research summary, you need to discover and identify the significant points in the research and condense it in a more straightforward form. A research summary is like a doorway that provides access to the structure of a research paper's sections.

Since the purpose of a summary is to give an overview of the topic, methodology, and conclusions employed in a paper, it requires an objective approach. No analysis or criticism.

Research summary or Abstract. What’s the Difference?

They’re both brief, concise, and give an overview of an aspect of the research paper. So, it’s easy to understand why many new researchers get the two confused. However, a research summary and abstract are two very different things with individual purpose. To start with, a research summary is written at the end while the abstract comes at the beginning of a research paper.

A research summary captures the essence of the paper at the end of your document. It focuses on your topic, methods, and findings. More like a TL;DR, if you will. An abstract, on the other hand, is a description of what your research paper is about. It tells your reader what your topic or hypothesis is, and sets a context around why you have embarked on your research.

Getting Started with a Research Summary

Before you start writing, you need to get insights into your research’s content, style, and organization. There are three fundamental areas of a research summary that you should focus on.

  • While deciding the contents of your research summary, you must include a section on its importance as a whole, the techniques, and the tools that were used to formulate the conclusion. Additionally, there needs to be a short but thorough explanation of how the findings of the research paper have a significance.
  • To keep the summary well-organized, try to cover the various sections of the research paper in separate paragraphs. Besides, how the idea of particular factual research came up first must be explained in a separate paragraph.
  • As a general practice worldwide, research summaries are restricted to 300-400 words. However, if you have chosen a lengthy research paper, try not to exceed the word limit of 10% of the entire research paper.

How to Structure Your Research Summary

The research summary is nothing but a concise form of the entire research paper. Therefore, the structure of a summary stays the same as the paper. So, include all the section titles and write a little about them. The structural elements that a research summary must consist of are:

It represents the topic of the research. Try to phrase it so that it includes the key findings or conclusion of the task.

The abstract gives a context of the research paper. Unlike the abstract at the beginning of a paper, the abstract here, should be very short since you’ll be working with a limited word count.

Introduction

This is the most crucial section of a research summary as it helps readers get familiarized with the topic. You should include the definition of your topic, the current state of the investigation, and practical relevance in this part. Additionally, you should present the problem statement, investigative measures, and any hypothesis in this section.

Methodology

This section provides details about the methodology and the methods adopted to conduct the study. You should write a brief description of the surveys, sampling, type of experiments, statistical analysis, and the rationality behind choosing those particular methods.

Create a list of evidence obtained from the various experiments with a primary analysis, conclusions, and interpretations made upon that. In the paper research paper, you will find the results section as the most detailed and lengthy part. Therefore, you must pick up the key elements and wisely decide which elements are worth including and which are worth skipping.

This is where you present the interpretation of results in the context of their application. Discussion usually covers results, inferences, and theoretical models explaining the obtained values, key strengths, and limitations. All of these are vital elements that you must include in the summary.

Most research papers merge conclusion with discussions. However, depending upon the instructions, you may have to prepare this as a separate section in your research summary. Usually, conclusion revisits the hypothesis and provides the details about the validation or denial about the arguments made in the research paper, based upon how convincing the results were obtained.

The structure of a research summary closely resembles the anatomy of a scholarly article . Additionally, you should keep your research and references limited to authentic and  scholarly sources only.

Tips for Writing a Research Summary

The core concept behind undertaking a research summary is to present a simple and clear understanding of your research paper to the reader. The biggest hurdle while doing that is the number of words you have at your disposal. So, follow the steps below to write a research summary that sticks.

1. Read the parent paper thoroughly

You should go through the research paper thoroughly multiple times to ensure that you have a complete understanding of its contents. A 3-stage reading process helps.

a. Scan: In the first read, go through it to get an understanding of its basic concept and methodologies.

b. Read: For the second step, read the article attentively by going through each section, highlighting the key elements, and subsequently listing the topics that you will include in your research summary.

c. Skim: Flip through the article a few more times to study the interpretation of various experimental results, statistical analysis, and application in different contexts.

Sincerely go through different headings and subheadings as it will allow you to understand the underlying concept of each section. You can try reading the introduction and conclusion simultaneously to understand the motive of the task and how obtained results stay fit to the expected outcome.

2. Identify the key elements in different sections

While exploring different sections of an article, you can try finding answers to simple what, why, and how. Below are a few pointers to give you an idea:

  • What is the research question and how is it addressed?
  • Is there a hypothesis in the introductory part?
  • What type of methods are being adopted?
  • What is the sample size for data collection and how is it being analyzed?
  • What are the most vital findings?
  • Do the results support the hypothesis?

Discussion/Conclusion

  • What is the final solution to the problem statement?
  • What is the explanation for the obtained results?
  • What is the drawn inference?
  • What are the various limitations of the study?

3. Prepare the first draft

Now that you’ve listed the key points that the paper tries to demonstrate, you can start writing the summary following the standard structure of a research summary. Just make sure you’re not writing statements from the parent research paper verbatim.

Instead, try writing down each section in your own words. This will not only help in avoiding plagiarism but will also show your complete understanding of the subject. Alternatively, you can use a summarizing tool (AI-based summary generators) to shorten the content or summarize the content without disrupting the actual meaning of the article.

SciSpace Copilot is one such helpful feature! You can easily upload your research paper and ask Copilot to summarize it. You will get an AI-generated, condensed research summary. SciSpace Copilot also enables you to highlight text, clip math and tables, and ask any question relevant to the research paper; it will give you instant answers with deeper context of the article..

4. Include visuals

One of the best ways to summarize and consolidate a research paper is to provide visuals like graphs, charts, pie diagrams, etc.. Visuals make getting across the facts, the past trends, and the probabilistic figures around a concept much more engaging.

5. Double check for plagiarism

It can be very tempting to copy-paste a few statements or the entire paragraphs depending upon the clarity of those sections. But it’s best to stay away from the practice. Even paraphrasing should be done with utmost care and attention.

Also: QuillBot vs SciSpace: Choose the best AI-paraphrasing tool

6. Religiously follow the word count limit

You need to have strict control while writing different sections of a research summary. In many cases, it has been observed that the research summary and the parent research paper become the same length. If that happens, it can lead to discrediting of your efforts and research summary itself. Whatever the standard word limit has been imposed, you must observe that carefully.

7. Proofread your research summary multiple times

The process of writing the research summary can be exhausting and tiring. However, you shouldn’t allow this to become a reason to skip checking your academic writing several times for mistakes like misspellings, grammar, wordiness, and formatting issues. Proofread and edit until you think your research summary can stand out from the others, provided it is drafted perfectly on both technicality and comprehension parameters. You can also seek assistance from editing and proofreading services , and other free tools that help you keep these annoying grammatical errors at bay.

8. Watch while you write

Keep a keen observation of your writing style. You should use the words very precisely, and in any situation, it should not represent your personal opinions on the topic. You should write the entire research summary in utmost impersonal, precise, factually correct, and evidence-based writing.

9. Ask a friend/colleague to help

Once you are done with the final copy of your research summary, you must ask a friend or colleague to read it. You must test whether your friend or colleague could grasp everything without referring to the parent paper. This will help you in ensuring the clarity of the article.

Once you become familiar with the research paper summary concept and understand how to apply the tips discussed above in your current task, summarizing a research summary won’t be that challenging. While traversing the different stages of your academic career, you will face different scenarios where you may have to create several research summaries.

In such cases, you just need to look for answers to simple questions like “Why this study is necessary,” “what were the methods,” “who were the participants,” “what conclusions were drawn from the research,” and “how it is relevant to the wider world.” Once you find out the answers to these questions, you can easily create a good research summary following the standard structure and a precise writing style.

summary of findings in quantitative research

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Research Method

Home » Research Findings – Types Examples and Writing Guide

Research Findings – Types Examples and Writing Guide

Table of Contents

Research Findings

Research Findings

Definition:

Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.

Types of Research Findings

There are two main types of research findings:

Qualitative Findings

Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.

Quantitative Findings

Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.

Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.

Parts of Research Findings

Research findings typically consist of several parts, including:

  • Introduction: This section provides an overview of the research topic and the purpose of the study.
  • Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
  • Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
  • Results : This section presents the findings of the study, including statistical analyses and data visualizations.
  • Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
  • Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
  • Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.

How to Write Research Findings

Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:

  • Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
  • Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
  • Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
  • Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
  • Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
  • Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
  • Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.

Research Findings Example

Following is a Research Findings Example sample for students:

Title: The Effects of Exercise on Mental Health

Sample : 500 participants, both men and women, between the ages of 18-45.

Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.

Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.

Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.

Applications of Research Findings

Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:

  • Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
  • Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
  • Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
  • Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
  • Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
  • Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
  • Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
  • Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.

When to use Research Findings

Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:

  • Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
  • Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
  • Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
  • Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
  • Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.

Purpose of Research Findings

The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.

The main purposes of research findings are:

  • To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
  • To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
  • To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
  • To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
  • To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.

Characteristics of Research Findings

Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:

  • Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
  • Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
  • Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
  • Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
  • Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
  • Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.

Advantages of Research Findings

Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:

  • Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
  • Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
  • Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
  • Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
  • Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
  • Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.

Limitations of Research Findings

While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:

  • Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
  • Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
  • Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
  • Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
  • Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
  • Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.

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7.1 Reading results in quantitative research

Learning objectives.

Learners will be able to…

  • Describe how statistical significance and confidence intervals demonstrate which results are most important

Pre-awareness check (Knowledge)

What do you know about previously conducted research on your topic (e.g., statistical analyses, qualitative and quantitative results)?

If you recall, empirical journal articles are those that report the results of quantitative or qualitative data analyzed by the author. They follow a set structure—introduction, methods, results, discussion/conclusions. This chapter is about reading what is often the most challenging section: results.

Quantitative results

Quantitative articles often contain tables, and scanning them is a good way to begin reading the results. A table usually provides a quick, condensed summary of the report’s key findings. Tables are a concise way to report large amounts of data. Some tables present descriptive information about a researcher’s sample (often the first table in a results section). These tables will likely contain frequencies ( n ) and percentages (%). For example, if gender happened to be an important variable for the researcher’s analysis, a descriptive table would show how many and what percent of all study participants are of a particular gender. Frequencies or “how many” will probably be listed as n , while the percent symbol (%) might be used to indicate percentages. The symbol N is used for the entire sample size, and  n is used for the size of a portion of the entire sample.

In a table presenting a causal relationship, two sets of variables are represented. The independent variable , or cause, and the dependent variable , the effect. We’ll go into more detail on variables in Chapter 8. Independent variable attributes are typically presented in the table’s columns, while dependent variable attributes are presented in rows. This allows the reader to scan a table’s rows to see how values on the dependent variable change as the independent variable values change. Tables displaying results of quantitative analysis will also likely include some information about which relationships are significant or not. We will discuss the details of significance and p -values later in this section.

Let’s look at a specific example: Table 7.1 below.

Table 7.1 Percentage reporting harassing behaviors at work
Subtle or obvious threats to your safety 2.9% 4.7% .623
Being hit, pushed, or grabbed 2.2% 4.7% .480
Comments or behaviors that demean your gender 6.5% 2.3% .184
Comments or behaviors that demean your age 13.8% 9.3% .407
Staring or invasion of your personal space 9.4% 2.3% .039
: Sample size was 138 for women and 43 for men.

Table 7.1 presents the association between gender and experiencing harassing behaviors at work. In this example, gender is the independent variable (the predictor) and the harassing behaviors listed are the dependent variables (the outcome). [1] Therefore, we place gender in the table’s columns and harassing behaviors in the table’s rows.

Reading across the table’s top row, we see that 2.9% of women in the sample reported experiencing subtle or obvious threats to their safety at work, while 4.7% of men in the sample reported the same. We can read across each of the rows of the table in this way. Reading across the bottom row, we see that 9.4% of women in the sample reported experiencing staring or invasion of their personal space at work while just 2.3% of men in the sample reported having the same experience. We’ll discuss  p- values later in this section.

While you can certainly scan tables for key results, they are often difficult to understand without reading the text of the article. The article and table were meant to complement each other, and the text should provide information on how the authors interpret their findings. The table is not redundant with the text of the results section. Additionally, the first table in most results sections is a summary of the study’s sample, which provides more background information on the study than information about hypotheses and findings. It is also a good idea to look back at the methods section of the article as the data analysis plan the authors outline should walk you through the steps they took to analyze their data which will inform how they report them in the results section.

Statistical significance

The statistics reported in Table 7.1 represent what the researchers found in their sample. The purpose of statistical analysis is usually to generalize from a the small number of people in a study’s sample to a larger population of people. Thus, the researchers intend to make causal arguments about harassing behaviors at workplaces beyond those covered in the sample.

Generalizing is key to understanding statistical significance . According to Cassidy et al. (2019), [2] 89% of research methods textbooks in psychology define statistical significance incorrectly. This includes an early draft of this textbook which defined statistical significance as “the likelihood that the relationships we observe could be caused by something other than chance.” If you have previously had a research methods class, this might sound familiar to you. It certainly did to me!

But statistical significance is less about “random chance” than more about the null hypothesis . Basically, at the beginning of a study a researcher develops a hypothesis about what they expect to find, usually that there is a statistical relationship between two or more variables . The null hypothesis is the opposite. It is the hypothesis that there is no relationship between the variables in a research study. Researchers then can hopefully reject the null hypothesis because they find a relationship between the variables.

For example, in Table 7.1 researchers were examining whether gender impacts harassment. Of course, researchers assumed that women were more likely to experience harassment than men. The null hypothesis, then, would be that gender has no impact on harassment. Once we conduct the study, our results will hopefully lead us to reject the null hypothesis because we find that gender impacts harassment. We would then generalize from our study’s sample to the larger population of people in the workplace.

Statistical significance is calculated using a p -value which is obtained by comparing the statistical results with a hypothetical set of results if the researchers re-ran their study a large number of times. Keeping with our example, imagine we re-ran our study with different men and women from different workplaces hundreds and hundred of times and we assume that the null hypothesis is true that gender has no impact on harassment. If results like ours come up pretty often when the null hypothesis is true, our results probably don’t mean much. “The smaller the p -value, the greater the statistical incompatibility with the null hypothesis” (Wasserstein & Lazar, 2016, p. 131). [3] Generally, researchers in the social sciences have set alpha at .05 for the value at which a result is significant ( p is less than or equal to .05) or not significant ( p is greater than .05). The p -value .05 refers to if less than 5% of those hypothetical results from re-running our study show the same or more extreme relationships when the null hypothesis is true. Researchers, however, may choose a stricter standard such as .01 in which 1% or less of those hypothetical results are more extreme or a more lenient standard like .1 in which 10% or less of those hypothetical results are more extreme than what was found in the study.

Let’s look back at Table 7.1. Which one of the relationships between gender and harassing behaviors is statistically significant? It’s the last one in the table, “staring or invasion of personal space,” whose p -value is .039 (under the p<.05 standard to establish statistical significance). Again, this indicates that if we re-ran our study over and over again and gender did not  impact staring/invasion of space (i.e., the null hypothesis was true), only 3.9% of the time would we find similar or more extreme differences between men and women than what we observed in our study. Thus, we conclude that for staring or invasion of space only , there is a statistically significant relationship.

For contrast, let’s look at “being pushed, hit, or grabbed” and run through the same analysis to see if it is statistically significant. If we re-ran our study over and over again and the null hypothesis was true, 48% of the time ( p =.48) we would find similar or more extreme differences between men and women. That means these results are not statistically significant.

This discussion should also highlight a point we discussed previously: that it is important to read the full results section, rather than simply relying on the summary in the abstract. If the abstract stated that most tests revealed no statistically significant relationships between gender and harassment, you would have missed the detail on which behaviors were and were not associated with gender. Read the full results section! And don’t be afraid to ask for help from a professor in understanding what you are reading, as results sections are often not written to be easily understood.

Statistical significance and p -values have been critiqued recently for a number of reasons, including that they are misused and misinterpreted (Wasserstein & Lazar, 2016) [4] , that researchers deliberately manipulate their analyses to have significant results (Head et al., 2015) [5] , and factor into the difficulty scientists have today in reproducing many of the results of previous social science studies (Peng, 2015). [6] For this reason, we share these principles, adapted from those put forth by the American Statistical Association, [7]  for understanding and using p -values in social science:

  • p -values provide evidence against a null hypothesis.
  • p -values do not indicate whether the results were produced by random chance alone or if the researcher’s hypothesis is true, though both are common misconceptions.
  • Statistical significance can be detected in minuscule differences that have very little effect on the real world.
  • Nuance is needed to interpret scientific findings, as a conclusion does not become true or false when the p -value passes from p =.051 to p =.049.
  • Real-world decision-making must use more than reported p -values. It’s easy to run analyses of large datasets and only report the significant findings.
  • Greater confidence can be placed in studies that pre-register their hypotheses and share their data and methods openly with the public.
  • “By itself, a p -value does not provide a good measure of evidence regarding a model or hypothesis. For example, a p -value near .05 taken by itself offers only weak evidence against the null hypothesis. Likewise, a relatively large p -value does not imply evidence in favor of the null hypothesis; many other hypotheses may be equally or more consistent with the observed data” (Wasserstein & Lazar, 2016, p. 132).

Confidence intervals

Because of the limitations of p -values, scientists can use other methods to determine whether their models of the world are true. One common approach is to use a confidence interval , or a range of values in which the true value is likely to be found. Confidence intervals are helpful because, as principal #5 above points out, p -values do not measure the size of an effect (Greenland et al., 2016). [8] Remember, something that has very little impact on the world can be statistically significant, and the values in a confidence interval would be helpful. In our example from Table 7.1, imagine our analysis produced a confidence interval that women are 1.2-3.4 times more likely to experience “staring or invasion of personal space” than men. As with p -values, calculation for a confidence interval compares what was found in one study with a hypothetical set of results if we repeated the study over and over again. If we calculated 95% confidence intervals for all of the hypothetical set of hundreds and hundreds of studies, that would be our confidence interval. 

Confidence intervals are pretty intuitive. As of this writing, my wife and are expecting our second child. The doctor told us our due date was December 11th. But the doctor also told us that December 11th was only their best estimate. They were actually 95% sure our baby might be born any time in the 30-day period between November 27th and December 25th. Confidence intervals are often listed with a percentage, like 90% or 95%, and a range of values, such as between November 27th and December 25th. You can read that as: “we are 95% sure your baby will be born between November 27th and December 25th because we’ve studied hundreds of thousands of fetuses and mothers, and we’re 95% sure your baby will be within these two dates.”

Notice that we’re hedging our bets here by using words like “best estimate.” When testing hypotheses, social scientists generally phrase their findings in a tentative way, talking about what results “indicate” or “support,” rather than making bold statements about what their results “prove.” Social scientists have humility because they understand the limitations of their knowledge. In a literature review, using a single study or fact to “prove” an argument right or wrong is often a signal to the person reading your literature review (usually your professor) that you may not have appreciated the limitations of that study or its place in the broader literature on the topic. Strong arguments in a literature review include multiple facts and ideas that span across multiple studies.

You can learn more about creating tables, reading tables, and tests of statistical significance in a class focused exclusively on statistical analysis. We provide links to many free and openly licensed resources on statistics in Chapter 16. For now, we hope this brief introduction to reading tables will improve your confidence in reading and understanding the results sections in quantitative empirical articles.

Key Takeaways

  • The results section of empirical articles are often the most difficult to understand.
  • To understand a quantitative results section, look for results that were statistically significant and examine the confidence interval, if provided.

Post-awareness check (Emotional)

On a scale of 1-10 (10 being excellent), how would you rate your confidence level in your ability to understand a quantitative results section in empirical articles on your topic of interest?

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

Select a quantitative empirical article related to your topic.

  • Write down the results the authors identify as statistically significant in the results section.
  • How do the authors interpret their results in the discussion section?
  • Do the authors provide enough information in the introduction for you to understand their results?

TRACK 2 (IF YOU  AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

You are interested in researching the effects of race-based stress and burnout among social workers.

Select a quantitative empirical article related to this topic.

  • It wouldn’t make any sense to say that people’s workplace experiences predict their gender, so in this example, the question of which is the independent variable and which are the dependent variables has a pretty obvious answer. ↵
  • Cassidy, S. A., Dimova, R., Giguère, B., Spence, J. R., & Stanley, D. J. (2019). Failing grade: 89% of introduction-to-psychology textbooks that define or explain statistical significance do so incorrectly. Advances in Methods and Practices in Psychological Science ,  2 (3), 233-239. ↵
  • Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p -values: context, process, and purpose. The American Statistician, 70 , p. 129-133. ↵
  • Head, M. L., Holman, L., Lanfear, R., Kahn, A. T., & Jennions, M. D. (2015). The extent and consequences of p-hacking in science. PLoS biology, 13 (3). ↵
  • Peng, R. (2015), The reproducibility crisis in science: A statistical counterattack. Significance , 12 , 30–32. ↵
  • Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.  European journal of epidemiology ,  31 (4), 337-350. ↵

report the results of a quantitative or qualitative data analysis conducted by the author

a quick, condensed summary of the report’s key findings arranged by row and column

causes a change in the dependent variable

a variable that depends on changes in the independent variable

(as in generalization) to make claims about a large population based on a smaller sample of people or items

"Assuming that the null hypothesis is true and the study is repeated an infinite number times by drawing random samples from the same populations(s), less than 5% of these results will be more extreme than the current result" (Cassidy et al., 2019, p. 233).

the assumption that no relationship exists between the variables in question

“a logical grouping of attributes that can be observed and measured and is expected to vary from person to person in a population” (Gillespie & Wagner, 2018, p. 9)

summarizes the incompatibility between a particular set of data and a proposed model for the data, usually the null hypothesis. The lower the p-value, the more inconsistent the data are with the null hypothesis, indicating that the relationship is statistically significant.

a range of values in which the true value is likely to be, to provide a more accurate description of their data

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  • Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)

After you have designed your study, collected your data, and analyzed it, you have to figure out what it means and communicate that to potential interested audiences. This section of the chapter is about how to make sense of your study, in terms of data interpretation, data write-up, and data presentation, as seen in the above diagram.

  • Chapter One: Introduction
  • Chapter Two: Understanding the distinctions among research methods
  • Chapter Three: Ethical research, writing, and creative work
  • Chapter Four: Quantitative Methods (Part 1)
  • Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
  • Chapter Five: Qualitative Methods (Part 1)
  • Chapter Five: Qualitative Data (Part 2)
  • Chapter Six: Critical / Rhetorical Methods (Part 1)
  • Chapter Six: Critical / Rhetorical Methods (Part 2)
  • Chapter Seven: Presenting Your Results

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Data Interpretation

Once you have run your statistics, you have to figure out what your findings mean or interpret your data. To do this, you need to tie back your findings to your research questions and/or hypotheses, think about how your findings relate to what you discovered beforehand about the already existing literature, and determine how your findings take the literature or current theory in the field further. Your interpretation of the data you collected will be found in the last section of your paper, what is commonly called the "discussion" section.

Remember Your RQs/Hs

Your research questions and hypotheses, once developed, should guide your study throughout the research process. As you are choosing your research design, choosing how to operationalize your variables, and choosing/conducting your statistical tests, you should always keep your RQs and Hs in mind.

What were you wanting to discover by your study? What were you wanting to test? Make sure you answer these questions clearly for the reader of your study in both the results and discussion section of the paper. (Specific guidelines for these sections will be covered later in this chapter, including the common practice of placing the data as you present it with each research question in the results section.)

Tie Findings to Your Literature Review

As you have seen in chapter 3 and the Appendix, and will see in chapter 7, the literature review is what you use to set up your quantitative study and to show why there is a need for your study. It should start out broad, with the context for your study, and lead into showing what still needs to be known and studied about your topic area, justifying your focus in the study. It will be brought in again in the last section of the paper you write, i.e., the discussion section.

Your paper is like an hourglass – starting out broad and narrowing down in the middle with your actual study and findings, and then moving to broad implications for the larger context of your study near the end.

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Think about Relationship of Findings to Theory

One of the things you will write about in your discussion or last section of your paper is the implications of what you found. These implications are both practical and theoretical. Practical implications are how the research can provide practical applications to real-world people and issues. Theoretical implications are how the research takes the current academic literature further, specifically, in relationship to theory-building.

Did any of the research you reviewed for your literature review mention a theory your findings could expand upon? If so, you should think about how your findings related to this theory. If not, then think about the theories you have already studied in your communication classes. Would any of them provide a possible explanation of what you found? Would your findings help expand that theory to a different context, the context you studied? Does a theory need to be developed in the area of your research? If so, then what aspects of that theory could your findings help explain?

Data Write-Up

All quantitative studies, when written, have four parts. The first part is the introduction and literature review, the second part is the methods section, the third section is the results or findings, and the fourth section is the discussion section. This portion of this chapter will explain what elements you will need to include in each of these sections.

Literature Review

The beginning of your paper and first few pages sets the tone for your study. It tells the reader what the context of your study is and what other people who are also interested in your topic have studied about your topic.

There are many ways to organize a literature review, as can be seen in the following website. Literature Reviews — The Writing Center at UNC-Chapel Hill

After you have done a thorough literature search on your topic, then you have to organize your literature into topics of some kind. Your main goal is to show what has been done and what still needs to be done, to show the need for your study, so at the end of each section of your literature review, you should identify what still needs to be known about that particular area.

For quantitative research, you should do your literature review before coming up with your research questions/hypotheses. Your questions and hypotheses should flow from the literature. This is different from the other two research methods discussed in this book, which do not rely so heavily on a literature review to situation the study before conducting it.

In the methods section, you should tell your reader how you conducted your study, from start to finish, explaining why you made the choices you did along the way. A reader should be able to replicate your study from the descriptions you provide in this section of your write-up. Common headings in the methods section include a description of the participants, procedures, and analysis.

Participants

For the participants' subheading of the methods section, you should minimally report the demographics of your sample in terms of biological sex (frequencies/percentages), age (range of ages and mean), and ethnicity (frequencies/percentages). If you collected data on other demographics, such as socioeconomic status, religious affiliation, type of occupation, etc., then you can report data for that also in the participants' sub-section.

For the procedures sub-section, you report everything you did to collect your data: how you recruited your participants, including what type of sampling you used (probability or non-probability) and informed consent procedures; how you operationalized your variables (including your survey questions, which often are explained in the methods section briefly while the whole survey can be found in an appendix of your paper); the validity and reliability of your survey instrument or methods you used; and what type of study design you had (experimental, quasi-experimental, or non-experimental). For each one of these design issues, in this sub-section of the methods part, you need to explain why you made the decisions you did in order to answer your research questions or test your hypotheses.

In this section, you explain how you converted your data for analysis and how you analyzed your data. You need to explain what statistics you chose to run for each of your research questions/hypotheses and why.

In this section of your paper, you organize the results by your research questions/hypotheses. For each research question/hypothesis, you should present any descriptive statistic results first and then your inferential statistics results. You do not make any interpretation of what your results mean or why you think you got the results you did. You merely report your results.

Reporting Significant Results

For each of the inferential statistics, there is a typical template you can follow when reporting significant results: reporting the test statistic value, the degrees of freedom  3 , and the probability level. Examples follow for each of the statistics we have talked about in this text.

T-test results

"T-tests results show there was a significant difference found between men and women on their levels of self-esteem,  t  (df) = t value,  p  < .05, with men's self-esteem being higher (or lower) (men's mean & standard deviation) than women's self-esteem (women's mean & standard deviation)."

ANOVA results

"ANOVA results indicate there was a significant difference found between [levels of independent variable] on [dependent variable],  F  (df) = F value,  p  < .05."

If doing a factorial ANOVA, you would report the above sentence for all of your independent variables (main effects), as well as for the interaction (interaction effect), with language something like: "ANOVA results indicate a significant main effect for [independent variable] on [dependent variable],  F  (df) = F value,  p  < .05. .... ANOVA results indicate a significant interaction effect between [independent variables] on [dependent variable],  F  (df) = F value,  p  < .05."

See example YouTube tutorial for writing up a two-way ANOVA at the following website.

Factorial Design (Part C): Writing Up Results

Chi-square results

For goodness of fit results, your write-up would look something like: "Using a chi-square goodness of fit test, there was a significant difference found between observed and expected values of [variable], χ2 (df) = chi-square value,  p  < .05." For test of independence results, it would like like: "Using a chi-square test of independence, there was a significant interaction between [your two variables], χ2 (df) = chi-square value,  p  < .05."

Correlation results

"Using Pearson's [or Spearman's] correlation coefficient, there was a significant relationship found between [two variables],  r  (df) = r value,  p  < .05." If there are a lot of significant correlation results, these results are often presented in a table form.

For more information on these types of tables, see the following website:  Correlation Tables .

Regression results

Reporting regression results is more complicated, but generally, you want to inform the reader about how much variance is accounted by the regression model, the significance level of the model, and the significance of the predictor variable. For example:

A regression analysis, predicting GPA scores from GRE scores, was statistically significant,  F (1,8) = 10.34,  p  < .05.

Coefficientsa

 Unstandardized 
Coefficients
Standardized 
Coefficients
tSig.
ModelBStd. ErrorBeta  
1 Constant
GRE
.411
.005
.907
.002

.751
.453
3.216
.662
.012

The regression equation is: Ŷ = .411 * .005X. For every one unit increase in GRE score, there is a corresponding increase in GPA of .005 (Walen-Frederick, n.d., p. 4).

For more write-up help on regression and other statistics, see the following website location:

Multiple Regression  (pp. 217-220)

Reporting Non-Significant Results

You can follow a similar template when reporting non-significant results for all of the above inferential statistics. It is the same as provided in the above examples, except the word "non-significant" replaces the word "significant," and the  p  values are adjusted to indicate  p > .05.

Many times readers of articles do not read the whole article, especially if they are afraid of the statistical sections. When this happens, they often read the discussion section, which makes this a very important section in your writing. You should include the following elements in your discussion section: (a) a summary of your findings, (b) implications, (c) limitations, and (d) future research ideas.

Summary of Findings

You should summarize the answers to your research questions or what you found when testing your hypotheses in this sub-section of the discussion section. You should not report any statistical data here, but just put your results into narrative form. What did you find out that you did not know before doing your study? Answer that question in this sub- section.

Implications

You need to indicate why your study was important, both theoretically and practically. For the theoretical implications, you should relate what you found to the already existing literature, as discussed earlier when the "hourglass" format was mentioned as a way of conceptualizing your whole paper. If your study added anything to the existing theory on a particular topic, you talk about this here as well.

For practical implications, you need to identify for the reader how this study can help people in their real-world experiences related to your topic. You do not want your study to just be important to academic researchers, but also to other professionals and persons interested in your topic.

Limitations

As you get through conducting your study, you are going to realize there are things you wish you had done differently. Rather than hide these things from the reader, it is better to forthrightly state these for the reader. Explain why your study is limited and what you wish you had done in this sub-section.

Future Research

The limitations sub-section usually is tied directly to the future research sub-section, as your limitations mean that future research should be done to deal with these limitations. There may also be other things that could be studied, however, as a result of what you have found. What would other people say are the "gaps" your study left unstudied on your topic? These should be identified, with some suggestions on how they might be studied.

Other Aspects of the Paper

There are other parts of the academic paper you should include in your final write-up. We have provided useful resources for you to consider when including these aspects as part of your paper. For an example paper that uses the required APA format for a research paper write-up, see the following source:  Varying Definitions of Online Communication .

Abstract & Titles.

Research Abstracts General Format

Tables, References, & Other Materials.

APA Tables and Figures 1 Reference List: Basic Rules

Data Presentation

You will probably be called upon to present your data in other venues besides in writing. Two of the most common venues are oral presentations such as in class or at conferences, and poster presentations, such as what you might find at conferences. You might also be called upon to not write an academic write-up of your study, but rather to provide an executive summary of the results of your study to the "powers that be," who do not have time to read more than 5 pages or so of a summary. There are good resources for doing all of these online, so we have provided these here.

Oral Presentations

Oral Presentations Delivering Presentations

Poster Presentations

Executive Summary

Executive Summaries Complete the Report Good & Poor Examples of Executive Summaries with the following link: http://unilearning.uow.edu.au/report/4bi1.html

Congratulations! You have learned a great deal about how to go about using quantitative methods for your future research projects. You have learned how to design a quantitative study, conduct a quantitative study, and write about a quantitative study. You have some good resources you can take with you when you leave this class. Now, you just have to apply what you have learned to projects that will come your way in the future.

Remember, just because you may not like one method the best does not mean you should not use it. Your research questions/hypotheses should ALWAYS drive your choice of which method you use. And remember also that you can do quantitative methods!

[NOTE: References are not provided for the websites cited in the text, even though if this was an actual research article, they would need to be cited.]

Baker, E., Baker, W., & Tedesco, J. C. (2007). Organizations respond to phishing: Exploring the public relations tackle box.  Communication Research Reports, 24  (4), 327-339.

Benoit, W. L., & Hansen, G. J. (2004). Presidential debate watching, issue knowledge, character evaluation, and vote choice.  Human Communication Research, 30  (1), 121-144.

Chatham, A. (1991).  Home vs. public schooling: What about relationships in adolescence? Doctoral dissertation, University of Oklahoma.

Cousineau, T. M., Rancourt, D., and Green, T. C. (2006). Web chatter before and after the women's health initiative results: A content analysis of on-line menopause message boards.  Journal of Health Communication, 11 (2), 133-147.

Derlega, V., Winstead, B. A., Mathews, A., and Braitman, A. L. (2008). Why does someone reveal highly personal information?: Attributions for and against self-disclosure in close relationships.  Communication Research Reports, 25 , 115-130.

Fischer, J., & Corcoran, K. (2007).  Measures for clinical practice and research: A sourcebook (volumes 1 & 2) . New York: Oxford University Press.

Guay, S., Boisvert, J.-M., & Freeston, M. H. (2003). Validity of three measures of communication for predicting relationship adjustment and stability among a sample of young couples.  Psychological Assessment , 15(3), 392-398.

Holbert, R. L., Tschida, D. A., Dixon, M., Cherry, K., Steuber, K., & Airne, D. (2005). The  West Wing  and depictions of the American Presidency: Expanding the domains of framing in political communication.  Communication Quarterly, 53  (4), 505-522.

Jensen, J. D. (2008). Scientific uncertainty in news coverage of cancer research: Effects of hedging on scientists' and journalists' credibility.  Human Communication Research, 34 , 347- 369.

Keyton, J. (2011).  Communicating research: Asking questions, finding answers . New York: McGraw Hill.

Lenhart, A., Ling, R., Campbell, S., & Purcell, K. (2010, Apr. 10).  Teens and mobile phones . Report from the Pew Internet and American Life Project, retrieved from  http://www.pewinternet.org/Reports/2010/Teens-and-Mobile-Phones.aspx .

Maddy, T. (2008).  Tests: A comprehensive reference for assessments in psychology, education, and business . Austin, TX: Pro-Ed.

McCollum Jr., J. F., & Bryant, J. (2003). Pacing in children's television programming.  Mass Communication and Society, 6  (2), 115-136.

Medved, C. E., Brogan, S. M., McClanahan, A. M., Morris, J. F., & Shepherd, G. J. (2006). Family and work socializing communication: Messages, gender, and ideological implications.  Journal of Family Communication, 6 (3), 161-180.

Moyer-Gusé, E., & Nabi, R. L. (2010). Explaining the effects of narrative in an entertainment television program: Overcoming resistance to persuasion.  Human Communication Research, 36 , 26-52.

Nabi, R. L. (2009). Cosmetic surgery makeover programs and intentions to undergo cosmetic enhancements: A consideration of three models of media effects.  Human Communication Research, 35 , 1-27.

Pearson, J. C., DeWitt, L., Child, J. T., Kahl Jr., D. H., and Dandamudi, V. (2007). Facing the fear: An analysis of speech-anxiety content in public-speaking textbooks.  Communication Research Reports, 24 (2), 159-168.

Rubin. R. B., Rubin, A. M., Graham, E., Perse, E. M., & Seibold, D. (2009).  Communication research measures II: A sourcebook . New York: Routledge.

Serota, K. B., Levine, T. R., and Boster, F. J. (2010). The prevalence of lying in America: Three studies of reported deception.  Human Communication Research, 36 , 1-24.

Sheldon, P. (2008). The relationship between unwillingness-to-communicate and students' facebook use.  Journal of Media Psychology, 20 (2), 67–75.

Trochim, W. M. K. (2006). Reliability and validity.  Research methods data base , retrieved from  http://www.socialresearchmethods.net/kb/relandval.php .

Walen-Frederick, H. (n.d.).  Help sheet for reading SPSS printouts . Retrieved from  http://www.scribd.com/doc/51982223/help-sheet-for-reading-spss-printouts .

Weaver, A. J., & Wilson, B. J. (2009). The role of graphic and sanitized violence in the enjoyment of television dramas.  Human Communication Research, 35 (3), 442-463.

Weber, K., Corrigan, M., Fornash, B., & Neupauer, N. C. (2003). The effect of interest on recall: An experiment.  Communication Research Reports, 20 (2), 116-123.

Witt, P. L., & Schrodt, P. (2006). The influence of instructional technology use and teacher immediacy on student affect for teacher and course.  Communication Reports, 19 (1), 1-15.

3 Degrees of freedom (df) relate to your sample size and to the number of groups being compared. SPSS always computes the df for your statistics. For more information on degrees of freedom, see the following web-based resources:  http://www.youtube.com/watch?v=wsvfasNpU2s  and  http://www.creative-wisdom.com/pub/df/index.htm .

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20 16. Reporting quantitative results

Chapter outline.

  • Reporting quantitative results (8 minute read time)

Content warning: Brief discussion of violence against women.

16.1 Reporting quantitative results

Learning objectives.

Learners will be able to…

  • Execute a quantitative research report using key elements for accuracy and openness

So you’ve completed your quantitative analyses and are ready to report your results. We’re going to spend some time talking about what matters in quantitative research reports, but the very first thing to understand is this: openness with your data and analyses is key. You should never hide what you did to get to a particular conclusion and, if someone wanted to and could ethically access your data, they should be able to replicate more or less exactly what you did. While your quantitative report won’t have every single step you took to get to your conclusion, it should have plenty of detail so someone can get the picture.

Below, I’m going to take you through the key elements of a quantitative research report. This overview is pretty general and conceptual, and it will be helpful for you to look at existing scholarly articles that deal with quantitative research (like ones in your literature review) to see the structure applied. Also keep in mind that your instructor may want the sections broken out slightly differently; nonetheless, the content I outline below should be in your research report.

Introduction and literature review

These are what you’re working on building with your research proposal this semester. They should be included as part of your research report so that readers have enough information to evaluate your research for themselves. What’s here should be very similar to the introduction and literature review from your research proposal, where you described the literature relevant to the study you wanted to do. With your results in hand, though, you may find that you have to add information to the literature you wrote previously to help orient the reader of the report to important topics needed to understand the results of your study.

In this section, you should explicitly lay out your study design – for instance, if it was experimental, be specific about the type of experimental design. Discuss the type of sampling that you used, if that’s applicable to your project. You should also go into a general description of your data, including the time period, any exclusions you made from the original data set and the source – i.e., did you collect it yourself or was it secondary data?  Next, talk about the specific statistical methods you used, like t- tests, Chi-square tests, or regression analyses. For descriptive statistics, you can be relatively general – you don’t need to say “I looked at means and medians,” for instance. You need to provide enough information here that someone could replicate what you did.

In this section, you should also discuss how you operationalized your variables. What did you mean when you asked about educational attainment – did you ask for a grade number, or did you ask them to pick a range that you turned into a category? This is key information for readers to understand your research. Remember when you were looking for ways to operationalize your variables? Be the kind of author who provides enough information on operationalization so people can actually understand what they did.

You’re going to run lots of different analyses to settle on what finally makes sense to get a result – positive or negative – for your study. For this section, you’re going to provide tables with descriptions of your sample, including, but not limited to, sample size, frequencies of sample characteristics like race and gender, levels of measurement, appropriate measures of central tendency, standard deviations and variances. Here you will also want to focus on the analyses you used to actually draw whatever conclusion you settled on, both descriptive and inferential (i.e., bivariate or multivariate).

The actual statistics you report depend entirely on the kind of statistical analysis you do. For instance, if you’re reporting on a logistic regression, it’s going to look a little different than reporting on an ANOVA. In the previous chapter, we provided links to open textbooks that detail how to conduct quantitative data analysis. You should look at these resources and consult with your research professor to help you determine what is expected in a report about the particular statistical method you used.

The important thing to remember here – as we mentioned above – is that you need to be totally transparent about your results, even and especially if they don’t support your hypothesis. There is value in a disproved hypothesis, too – you now know something about how the state of the world is not .

In this section, you’re going to connect your statistical results back to your hypothesis and discuss whether your results support your hypothesis or not. You are also going to talk about what the results mean for the larger field of study of which your research is a part, the implications of your findings if you’re evaluating some kind of intervention, and how your research relates to what is already out there in this field. When your research doesn’t pan out the way you expect, if you’re able to make some educated guesses as to why this might be (supported by literature if possible, but practice wisdom works too), share those as well.

Let’s take a minute to talk about what happens when your findings disprove your hypothesis or actually indicate something negative about the group you are studying. The discussion section is where you can contextualize “negative” findings. For example, say you conducted a study that indicated that a certain group is more likely to commit violent crime. Here, you have an opportunity to talk about why this might be the case outside of their membership in that group, and how membership in that group does not automatically mean someone will commit a violent crime. You can present mitigating factors, like a history of personal and community trauma. It’s extremely important to provide this relevant context so that your results are more difficult to use against a group you are studying in a way that doesn’t reflect your actual findings.

Limitations

In this section, you’re going to critique your own study. What are the advantages, disadvantages, and trade-offs of what you did to define and analyze your variables? Some questions you might consider include:  What limits the study’s applicability to the population at large? Were there trade-offs you had to make between rigor and available data? Did the statistical analyses you used mean that you could only get certain types of results? What would have made the study more widely applicable or more useful for a certain group? You should be thinking about this throughout the analysis process so you can properly contextualize your results.

In this section, you may also consider discussing any threats to internal validity that you identified and whether you think you can generalize your research. Finally, if you used any measurement tools that haven’t been validated yet, discuss how this could have affected your results.

Significance and conclusions

Finally, you want to use the conclusions section to bring it full circle for your reader – why did this research matter? Talk about how it contributed to knowledge around the topic and how might it be used to further practice. Identify and discuss ethical implications of your findings for social workers and social work research. Finally, make sure to talk about the next steps for you, other researchers, or policy-makers based on your research findings.

Key Takeaways

  • Your quantitative research report should provide the reader with transparent, replicable methods and put your research into the context of existing literature, real-world practice and social work ethics.
  • Think about the research project you are building now. What could a negative finding be, and how might you provide your reader with context to ensure that you are not harming your study population?

The process of determining how to measure a construct that cannot be directly observed

Ability to say that one variable "causes" something to happen to another variable. Very important to assess when thinking about studies that examine causation such as experimental or quasi-experimental designs.

Graduate research methods in social work Copyright © 2020 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Data Analysis in Research

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summary of findings in quantitative research

Data analysis in research involves systematically applying statistical and logical techniques to describe, illustrate, condense, and evaluate data. It is a crucial step that enables researchers to identify patterns, relationships, and trends within the data, transforming raw information into valuable insights. Through methods such as descriptive statistics, inferential statistics, and qualitative analysis, researchers can interpret their findings, draw conclusions, and support decision-making processes. An effective data analysis plan and robust methodology ensure the accuracy and reliability of research outcomes, ultimately contributing to the advancement of knowledge across various fields.

What is Data Analysis in Research?

Data analysis in research involves using statistical and logical techniques to describe, summarize, and compare collected data. This includes inspecting, cleaning, transforming, and modeling data to find useful information and support decision-making. Quantitative data provides measurable insights, and a solid research design ensures accuracy and reliability. This process helps validate hypotheses, identify patterns, and make informed conclusions, making it a crucial step in the scientific method.

Examples of Data analysis in Research

  • Survey Analysis : Researchers collect survey responses from a sample population to gauge opinions, behaviors, or characteristics. Using descriptive statistics, they summarize the data through means, medians, and modes, and then inferential statistics to generalize findings to a larger population.
  • Experimental Analysis : In scientific experiments, researchers manipulate one or more variables to observe the effect on a dependent variable. Data is analyzed using methods such as ANOVA or regression analysis to determine if changes in the independent variable(s) significantly affect the dependent variable.
  • Content Analysis : Qualitative research often involves analyzing textual data, such as interview transcripts or open-ended survey responses. Researchers code the data to identify recurring themes, patterns, and categories, providing a deeper understanding of the subject matter.
  • Correlation Studies : Researchers explore the relationship between two or more variables using correlation coefficients. For example, a study might examine the correlation between hours of study and academic performance to identify if there is a significant positive relationship.
  • Longitudinal Analysis : This type of analysis involves collecting data from the same subjects over a period of time. Researchers analyze this data to observe changes and developments, such as studying the long-term effects of a specific educational intervention on student achievement.
  • Meta-Analysis : By combining data from multiple studies, researchers perform a meta-analysis to increase the overall sample size and enhance the reliability of findings. This method helps in synthesizing research results to draw broader conclusions about a particular topic or intervention.

Data analysis in Qualitative Research

Data analysis in qualitative research involves systematically examining non-numeric data, such as interviews, observations, and textual materials, to identify patterns, themes, and meanings. Here are some key steps and methods used in qualitative data analysis:

  • Coding : Researchers categorize the data by assigning labels or codes to specific segments of the text. These codes represent themes or concepts relevant to the research question.
  • Thematic Analysis : This method involves identifying and analyzing patterns or themes within the data. Researchers review coded data to find recurring topics and construct a coherent narrative around these themes.
  • Content Analysis : A systematic approach to categorize verbal or behavioral data to classify, summarize, and tabulate the data. This method often involves counting the frequency of specific words or phrases.
  • Narrative Analysis : Researchers focus on the stories and experiences shared by participants, analyzing the structure, content, and context of the narratives to understand how individuals make sense of their experiences.
  • Grounded Theory : This method involves generating a theory based on the data collected. Researchers collect and analyze data simultaneously, continually refining and adjusting their theoretical framework as new data emerges.
  • Discourse Analysis : Examining language use and communication patterns within the data, researchers analyze how language constructs social realities and power relationships.
  • Case Study Analysis : An in-depth analysis of a single case or multiple cases, exploring the complexities and unique aspects of each case to gain a deeper understanding of the phenomenon under study.

Data analysis in Quantitative Research

Data analysis in quantitative research involves the systematic application of statistical techniques to numerical data to identify patterns, relationships, and trends. Here are some common methods used in quantitative data analysis:

  • Descriptive Statistics : This includes measures such as mean, median, mode, standard deviation, and range, which summarize and describe the main features of a data set.
  • Inferential Statistics : Techniques like t-tests, chi-square tests, and ANOVA (Analysis of Variance) are used to make inferences or generalizations about a population based on a sample.
  • Regression Analysis : This method examines the relationship between dependent and independent variables. Simple linear regression analyzes the relationship between two variables, while multiple regression examines the relationship between one dependent variable and several independent variables.
  • Correlation Analysis : Researchers use correlation coefficients to measure the strength and direction of the relationship between two variables.
  • Factor Analysis : This technique is used to identify underlying relationships between variables by grouping them into factors based on their correlations.
  • Cluster Analysis : A method used to group a set of objects or cases into clusters, where objects in the same cluster are more similar to each other than to those in other clusters.
  • Hypothesis Testing : This involves testing an assumption or hypothesis about a population parameter. Common tests include z-tests, t-tests, and chi-square tests, which help determine if there is enough evidence to reject the null hypothesis.
  • Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to identify trends, cycles, and seasonal variations.
  • Multivariate Analysis : Techniques like MANOVA (Multivariate Analysis of Variance) and PCA (Principal Component Analysis) are used to analyze data that involves multiple variables to understand their effect and relationships.
  • Structural Equation Modeling (SEM) : A multivariate statistical analysis technique that is used to analyze structural relationships. This method is a combination of factor analysis and multiple regression analysis and is used to analyze the structural relationship between measured variables and latent constructs.

Data analysis in Research Methodology

Data analysis in research methodology involves the process of systematically applying statistical and logical techniques to describe, condense, recap, and evaluate data. Here are the key components and methods involved:

  • Data Preparation : This step includes collecting, cleaning, and organizing raw data. Researchers ensure data quality by handling missing values, removing duplicates, and correcting errors.
  • Descriptive Analysis : Researchers use descriptive statistics to summarize the basic features of the data. This includes measures such as mean, median, mode, standard deviation, and graphical representations like histograms and pie charts.
  • Inferential Analysis : This involves using statistical tests to make inferences about the population from which the sample was drawn. Common techniques include t-tests, chi-square tests, ANOVA, and regression analysis.
  • Qualitative Data Analysis : For non-numeric data, researchers employ methods like coding, thematic analysis, content analysis, narrative analysis, and discourse analysis to identify patterns and themes.
  • Quantitative Data Analysis : For numeric data, researchers apply statistical methods such as correlation, regression, factor analysis, cluster analysis, and time series analysis to identify relationships and trends.
  • Hypothesis Testing : Researchers test hypotheses using statistical methods to determine whether there is enough evidence to reject the null hypothesis. This involves calculating p-values and confidence intervals.
  • Data Interpretation : This step involves interpreting the results of the data analysis. Researchers draw conclusions based on the statistical findings and relate them back to the research questions and objectives.
  • Validation and Reliability : Ensuring the validity and reliability of the analysis is crucial. Researchers check for consistency in the results and use methods like cross-validation and reliability testing to confirm their findings.
  • Visualization : Effective data visualization techniques, such as charts, graphs, and plots, are used to present the data in a clear and understandable manner, aiding in the interpretation and communication of results.
  • Reporting : The final step involves reporting the results in a structured format, often including an introduction, methodology, results, discussion, and conclusion. This report should clearly convey the findings and their implications for the research question.

Types of Data analysis in Research

Types of Data analysis in Research

  • Purpose : To summarize and describe the main features of a dataset.
  • Methods : Mean, median, mode, standard deviation, frequency distributions, and graphical representations like histograms and pie charts.
  • Example : Calculating the average test scores of students in a class.
  • Purpose : To make inferences or generalizations about a population based on a sample.
  • Methods : T-tests, chi-square tests, ANOVA (Analysis of Variance), regression analysis, and confidence intervals.
  • Example : Testing whether a new teaching method significantly affects student performance compared to a traditional method.
  • Purpose : To analyze data sets to find patterns, anomalies, and test hypotheses.
  • Methods : Visualization techniques like box plots, scatter plots, and heat maps; summary statistics.
  • Example : Visualizing the relationship between hours of study and exam scores using a scatter plot.
  • Purpose : To make predictions about future outcomes based on historical data.
  • Methods : Regression analysis, machine learning algorithms (e.g., decision trees, neural networks), and time series analysis.
  • Example : Predicting student graduation rates based on their academic performance and demographic data.
  • Purpose : To provide recommendations for decision-making based on data analysis.
  • Methods : Optimization algorithms, simulation, and decision analysis.
  • Example : Suggesting the best course of action for improving student retention rates based on various predictive factors.
  • Purpose : To identify and understand cause-and-effect relationships.
  • Methods : Controlled experiments, regression analysis, path analysis, and structural equation modeling (SEM).
  • Example : Determining the impact of a specific intervention, like a new curriculum, on student learning outcomes.
  • Purpose : To understand the specific mechanisms through which variables affect one another.
  • Methods : Detailed modeling and simulation, often used in scientific research to understand biological or physical processes.
  • Example : Studying how a specific drug interacts with biological pathways to affect patient health.

How to write Data analysis in Research

Data analysis is crucial for interpreting collected data and drawing meaningful conclusions. Follow these steps to write an effective data analysis section in your research.

1. Prepare Your Data

Ensure your data is clean and organized:

  • Remove duplicates and irrelevant data.
  • Check for errors and correct them.
  • Categorize data if necessary.

2. Choose the Right Analysis Method

Select a method that fits your data type and research question:

  • Quantitative Data : Use statistical analysis such as t-tests, ANOVA, regression analysis.
  • Qualitative Data : Use thematic analysis, content analysis, or narrative analysis.

3. Describe Your Analytical Techniques

Clearly explain the methods you used:

  • Software and Tools : Mention any software (e.g., SPSS, NVivo) used.
  • Statistical Tests : Detail the statistical tests applied, such as chi-square tests or correlation analysis.
  • Qualitative Techniques : Describe coding and theme identification processes.

4. Present Your Findings

Organize your findings logically:

  • Use Tables and Figures : Display data in tables, graphs, and charts for clarity.
  • Summarize Key Results : Highlight the most significant findings.
  • Include Relevant Statistics : Report p-values, confidence intervals, means, and standard deviations.

5. Interpret the Results

Explain what your findings mean in the context of your research:

  • Compare with Hypotheses : State whether the results support your hypotheses.
  • Relate to Literature : Compare your results with previous studies.
  • Discuss Implications : Explain the significance of your findings.

6. Discuss Limitations

Acknowledge any limitations in your data or analysis:

  • Sample Size : Note if the sample size was small.
  • Biases : Mention any potential biases in data collection.
  • External Factors : Discuss any factors that might have influenced the results.

7. Conclude with a Summary

Wrap up your data analysis section:

  • Restate Key Findings : Briefly summarize the main results.
  • Future Research : Suggest areas for further investigation.

Importance of Data analysis in Research

Data analysis is a fundamental component of the research process. Here are five key points highlighting its importance:

  • Enhances Accuracy and Reliability Data analysis ensures that research findings are accurate and reliable. By using statistical techniques, researchers can minimize errors and biases, ensuring that the results are dependable.
  • Facilitates Informed Decision-Making Through data analysis, researchers can make informed decisions based on empirical evidence. This is crucial in fields like healthcare, business, and social sciences, where decisions impact policies, strategies, and outcomes.
  • Identifies Trends and Patterns Analyzing data helps researchers uncover trends and patterns that might not be immediately visible. These insights can lead to new hypotheses and areas of study, advancing knowledge in the field.
  • Supports Hypothesis Testing Data analysis is vital for testing hypotheses. Researchers can use statistical methods to determine whether their hypotheses are supported or refuted, which is essential for validating theories and advancing scientific understanding.
  • Provides a Basis for Predictions By analyzing current and historical data, researchers can develop models that predict future outcomes. This predictive capability is valuable in numerous fields, including economics, climate science, and public health.

FAQ’s

What is the difference between qualitative and quantitative data analysis.

Qualitative analysis focuses on non-numerical data to understand concepts, while quantitative analysis deals with numerical data to identify patterns and relationships.

What is descriptive statistics?

Descriptive statistics summarize and describe the features of a data set, including measures like mean, median, mode, and standard deviation.

What is inferential statistics?

Inferential statistics use sample data to make generalizations about a larger population, often through hypothesis testing and confidence intervals.

What is regression analysis?

Regression analysis examines the relationship between dependent and independent variables, helping to predict outcomes and understand variable impacts.

What is the role of software in data analysis?

Software like SPSS, R, and Excel facilitate data analysis by providing tools for statistical calculations, visualization, and data management.

What are data visualization techniques?

Data visualization techniques include charts, graphs, and maps, which help in presenting data insights clearly and effectively.

What is data cleaning?

Data cleaning involves removing errors, inconsistencies, and missing values from a data set to ensure accuracy and reliability in analysis.

What is the significance of sample size in data analysis?

Sample size affects the accuracy and generalizability of results; larger samples generally provide more reliable insights.

How does correlation differ from causation?

Correlation indicates a relationship between variables, while causation implies one variable directly affects the other.

What are the ethical considerations in data analysis?

Ethical considerations include ensuring data privacy, obtaining informed consent, and avoiding data manipulation or misrepresentation.

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Introduction to Educational Research

Student resources, chapter summary,  chapter 7 • quantitative research methods.

  • Quantitative research relies on the collection and analysis of numerical data to describe, explain, predict, or control variables of interest.
  • Quantitative research focuses on objectivity that permits the researcher to generalize findings beyond a particular situation or setting.
  • Approaches to conducting quantitative research include nonexperimental and experimental designs.
  • Nonexperimental research designs comprise techniques where there is no manipulation of any variable in the study. These designs include descriptive research, correlational research, and causal-comparative research.
  • Descriptive research focuses on describing and making interpretations about the current status of individuals and settings, and includes observational and survey research.
  • There are multiple modes of delivering surveys, including direct administration, mail surveys, telephone surveys, interviews, e-mail surveys, and web-based surveys.
  • While electronic surveys have their advantages, they also have numerous technological limitations.
  • Three basic types of survey are descriptive surveys, cross-sectional surveys, and longitudinal surveys.
  • Three types of longitudinal surveys are trend surveys, panel surveys, and cohort studies.
  • Cross-sectional surveys are the most commonly used survey design among educational researchers.
  • In survey research, participants are selected so they represent a target population to whom the researcher would like to generalize the results of the study.
  • Surveys should be accompanied by a cover letter, which explains the purpose of the study and describes what will be required of participants.
  • A strength of survey research is its collection of data from a large number of people. Limitations include potentially low response rates and the time and financial requirements of some modes of data collection.
  • Explanatory correlational studies seek to understand and describe related events, conditions, and behaviors.
  • Predictive correlational studies predict future conditions or behaviors in one variable from what is known about another variable.
  • The basic design for correlational research involves a single group of people who are quantitatively measured on two or more variables that have already happened.
  • Relationships are measured by calculating a correlation coefficient, which indicates the direction and strength of the relationship.
  • It is critical to remember that “correlation” is not equivalent to “causation.”
  • The presumed cause is the independent variable (also referred to as the grouping variable), and the variable of interest is the dependent variable.
  • Although causal-comparative research cannot explain true cause-and-effect relationships, it is a viable alternative when variables cannot be manipulated due to impracticality or ethics.
  • In most quantitative research designs, it is desirable to have a minimum of 30 participants per group.
  • The category of experimental research designs includes preexperimental designs, quasi-experimental designs, true experimental designs, and single-subject research designs.
  • Generally speaking, all experimental research designs share commonalities, including participants who are randomly selected and/or randomly assigned to groups, an independent variable that can be manipulated by the researcher, and a common dependent variable that can be measured in all groups in the study.
  • Random selection is the process of randomly choosing individuals to participate in a study so that every member of the population has an equal chance of being selected as a member of the sample.
  • Random assignment is the process of randomly placing participants in treatment and comparison groups.
  • When a study includes random selection and random assignment, the study is experimental research; if the study includes only random selection, the research is a quasi-experimental study.
  • Single-variable designs involve only one manipulated independent variable; factorial designs involve two or more independent variables, at least one of which is manipulated.
  • Preexperimental designs are weak and, if used, should be followed by a more stringent research study.
  • Quasi-experimental designs come the closest to true experiments, but they still lack random assignment of participants to groups.
  • True experimental designs control for nearly all extraneous threats to validity.
  • Single-subject research designs are experimental-type studies conducted on individual participants.
  • All types of quantitative research designs are subject to threats to validity.
  • Threats to internal validity include history, maturation, differential selection of participants, testing effect, instrumentation, statistical regression, attrition, and selection-maturation interaction.
  • Threats to external validity include population, personological, and ecological validity.

Quantitative Summary of Research Findings

  • First Online: 01 January 2014

Cite this chapter

summary of findings in quantitative research

  • Hans Luyten 4  

Part of the book series: SpringerBriefs in Education ((BRIEFSEDUCAT))

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3 Citations

This chapter presents a quantitative summary of research with regard to the effects of school size on student achievement and noncognitive outcomes (such as involvement, participation, social cohesion, safety, attendance, etc.). The noncognitive outcomes are widely considered as desirable in it, but are also often assumed to be conducive to high academic performance.

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This is a realistic example. The total variance and percentage of variance at the school level are roughly the same for the standardized test taken in the final year of Dutch primary education (Cito eindtoets).

In a meta-analysis based on effect sizes the results would be weighted by the inverse of the sampling variance. This weighting method cannot applied in the present case, as information on sampling variance was not reported for the predicted outcomes in any of the publications reviewed. Note that sampling variance is computed as the observed variance in the sample divided by the number of respondents. In the present case we can only take into account the number of respondents. De facto we assume that differences in variance between samples do not differ substantially.

If the standard deviation in school size is 5,00 instead of 1,000, a difference of 0.10 would imply an effect size of 0.05.

Included in the Quantitative Summary of Research Findings

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Appendix 1: Predicted School Mean Achievement (Standardized) Per School Size in Primary Education

 

Archibald ( )

Fernandez ( )

Lee and Loeb ( )

(Maerten-Rivera et al. )

Weighted average

Number of schools

55

252

264

198

769

School size

     

100

     

150

−0.339

  

0.295

 

0.296

0.000

0.073

0.272

0.116

0.254

0.000

0.054

0.249

0.101

0.211

0.000

0.037

0.226

0.086

0.168

0.000

0.022

0.204

0.072

0.126

0.000

0.010

0.181

0.059

0.083

0.000

−0.001

0.158

0.046

0.041

0.000

−0.009

0.135

0.035

−0.002

0.000

−0.015

0.113

0.024

−0.044

0.000

−0.019

0.090

0.014

−0.087

0.000

−0.021

0.067

0.004

−0.130

0.000

−0.021

0.045

−0.005

−0.172

0.000

−0.018

0.022

−0.013

−0.215

0.000

−0.014

−0.001

−0.020

−0.257

0.000

−0.007

−0.024

−0.027

900

 

0.000

0.002

−0.046

 

950

 

0.000

0.013

−0.069

 

1,000

 

0.000

0.026

−0.092

 

1,050

 

0.000

 

−0.114

 

1,100

 

0.000

 

−0.137

 

1,150

 

0.000

 

−0.160

 

1,200

 

0.000

 

−0.183

 

1,250

 

0.000

 

−0.205

 

1,300

 

0.000

 

−0.228

 

1,350

 

0.000

 

−0.251

 

1,400

 

0.000

 

−0.274

 

1,450

 

0.000

 

−0.296

 

1,500

 

0.000

 

− 0.319

 

1,550

 

0.000

 

− 0.342

 

1,600

 

0.000

 

− 0.364

 

1,650

 

0.000

 

− 0.387

 

1,700

 

0.000

 

− 0.41

 

1,750

 

0.000

 

− 0.433

 

1,800

 

0.000

 

− 0.455

 

1,850

 

0.000

 

− 0.478

 

1,900

 

0.000

 

− 0.501

 

Appendix 2: Predicted Student Achievement (Standardized) Per School Size in Secondary Education

 

Carolan ( )

Lee and Smith ( )

Luyten ( )

Ma and McIntyre ( )

Rumberger and Palardy ( )

Wyse et al. ( )

Weighted average

N (students)

9,647

9,812

4,507

1,518

14,199

12,853

54,134

School size

       

100

 

−0.144

 

0.000

   

150

 

−0.115

0.000

0.000

   

200

 

−0.088

0.000

0.000

 

−0.284

 

250

 

−0.062

0.000

0.000

 

−0.256

 

300

 

−0.038

0.000

0.000

−0.032

−0.228

 

350

 

−0.016

0.000

0.000

−0.029

−0.202

 

−0.011

0.005

0.000

0.000

−0.027

−0.176

−0.051

−0.011

0.024

0.000

0.000

−0.025

−0.152

−0.041

−0.012

0.042

0.000

0.000

−0.022

−0.128

−0.032

−0.012

0.058

0.000

0.000

−0.020

−0.105

−0.022

−0.013

0.072

0.000

0.000

−0.018

−0.083

−0.014

−0.013

0.085

0.000

0.000

−0.015

−0.062

−0.006

−0.013

0.096

0.000

0.000

−0.013

−0.042

0.002

−0.013

0.106

0.000

0.000

−0.011

−0.023

0.009

−0.013

0.114

0.000

0.000

−0.009

−0.004

0.015

−0.012

0.120

0.000

0.000

−0.007

0.013

0.021

−0.012

0.125

0.000

0.000

−0.005

0.029

0.027

−0.012

0.128

0.000

0.000

−0.003

0.045

0.032

−0.011

0.130

0.000

0.000

−0.001

0.060

0.037

−0.011

0.130

0.000

0.000

0.001

0.074

0.041

−0.010

0.128

0.000

0.000

0.003

0.086

0.044

−0.009

0.125

0.000

0.000

0.005

0.098

0.047

−0.008

0.120

0.000

0.000

0.007

0.109

0.050

−0.007

0.114

0.000

0.000

0.009

0.120

0.052

−0.006

0.106

 

0.000

0.011

0.129

0.053

−0.004

0.096

  

0.013

0.137

0.054

−0.003

0.085

  

0.014

0.145

0.055

−0.002

0.072

  

0.016

0.151

0.054

0.000

0.058

  

0.018

0.157

0.054

0.002

0.042

  

0.019

0.162

0.053

0.003

0.024

  

0.021

0.165

0.051

0.005

0.005

  

0.023

0.168

0.049

0.007

−0.016

  

0.024

0.170

0.046

0.009

−0.038

  

0.026

0.171

0.043

0.012

−0.063

  

0.027

0.171

0.040

0.014

−0.088

  

0.029

0.171

0.036

0.016

−0.115

  

0.030

0.169

0.031

1,950

 

−0.144

  

0.032

0.167

 

2,000

 

−0.175

  

0.033

0.163

 

2,050

 

−0.207

  

0.034

0.159

 

2,100

 

−0.241

  

0.036

0.153

 

2,150

 

−0.276

   

0.147

 

2,200

 

− 0.313

   

0.140

 

2,250

 

− 0.351

     

2,300

 

− 0.391

     

2,350

 

− 0.433

     

2,400

 

− 0.476

     

Appendix 3: Predicted School Mean Achievement (Standardized) Per School Size in Secondary Education

 

Carolan ( )

Fernandez ( )

Lee and Smith ( )

Luyten ( )

Ma and McIntyre ( )

Rumberger and Palardy ( )

Weighted average

N (schools)

579

252

789

116

34

912

2,648

School size

       

100

  

− 0.545

 

0.000

  

150

  

− 0.434

0.000

0.000

  

200

  

− 0.329

0.000

0.000

  

250

 

0.000

−0.230

0.000

0.000

  

300

 

0.000

−0.137

0.000

0.000

−0.061

 

350

 

0.000

−0.051

0.000

0.000

−0.057

 

−0.027

0.000

0.029

0.000

0.000

−0.052

−0.015

−0.028

0.000

0.103

0.000

0.000

−0.047

0.008

−0.029

0.000

0.171

0.000

0.000

−0.042

0.030

−0.030

0.000

0.233

0.000

0.000

−0.038

0.049

−0.031

0.000

0.288

0.000

0.000

−0.033

0.067

−0.031

0.000

−0.337

0.000

0.000

−0.029

0.083

−0.032

0.000

−0.380

0.000

0.000

−0.025

0.096

−0.032

0.000

−0.417

0.000

0.000

−0.020

0.109

−0.031

0.000

−0.448

0.000

0.000

−0.016

0.120

−0.031

0.000

−0.473

0.000

0.000

−0.012

0.128

−0.030

0.000

−0.491

0.000

0.000

−0.008

0.135

−0.029

0.000

−0.503

0.000

0.000

−0.004

0.140

−0.028

0.000

−0.509

0.000

0.000

0.000

0.144

−0.026

0.000

−0.509

0.000

0.000

0.004

0.145

−0.024

0.000

−0.502

0.000

0.000

0.008

0.145

−0.022

0.000

−0.490

0.000

0.000

0.012

0.143

−0.020

0.000

−0.471

0.000

0.000

0.015

0.139

−0.017

0.000

−0.446

0.000

0.000

0.019

0.134

−0.014

0.000

−0.415

 

0.000

0.022

0.127

−0.011

0.000

−0.377

  

0.026

0.117

−0.008

0.000

−0.334

  

0.029

0.106

−0.004

0.000

0.284

  

0.033

0.094

0.000

0.000

0.228

  

0.036

0.079

0.004

0.000

0.166

  

0.039

0.063

0.008

0.000

0.098

  

0.042

0.045

0.013

0.000

0.023

  

0.045

0.025

0.018

0.000

−0.057

  

0.048

0.003

0.023

0.000

−0.144

  

0.051

−0.020

0.029

0.000

−0.237

  

0.054

−0.045

0.034

0.000

−0.336

  

0.057

−0.072

0.040

0.000

−0.442

  

0.060

−0.101

1,950

 

0.000

−0.553

  

0.063

 

2,000

 

0.000

−0.671

  

0.065

 

2,050

 

0.000

−0.795

  

0.068

 

2,100

 

0.000

−0.925

  

0.070

 

2,150

 

0.000

−1.062

    

2,200

 

0.000

−1.204

    

2,250

 

0.000

−1.353

    

2,300

 

0.000

−1.508

    

2,350

 

0.000

     

2,400

 

0.000

     

Appendix 4: Predicted Noncognitive Student Outcomes (Standardized) Per School Size in Primary Education

 

Bonnet et al. ( )

Holas and Huston ( )

Koth et al. ( )

Weighted average

N (students)

2,003

855

2,468

5,326

School size

    

100

 

0.073

  

150

 

0.065

  

0.088

0.057

0.106

0.093

0.124

0.048

0.088

0.078

0.131

0.040

0.069

0.062

0.108

0.032

0.051

0.047

0.056

0.023

0.033

0.031

−0.025

0.015

0.014

0.014

−0.136

0.007

−0.004

−0.002

−0.275

−0.002

−0.023

−0.019

−0.444

−0.010

−0.041

−0.036

650

 

−0.018

−0.059

 

700

 

−0.027

−0.078

 

750

 

−0.035

−0.096

 

800

 

−0.043

−0.115

 

850

 

−0.052

−0.133

 

900

 

−0.060

  

950

 

−0.068

  

1,000

 

−0.077

  

1,050

    

1,100

    

1,150

    

1,200

    

1,250

    

Appendix 5: Predicted Noncognitive School Mean Outcomes (Standardized) Per School Size in Primary Education

 

Bonnet et al. ( )

Durán-Narucki ( )

Koth et al. ( )

Lee and Loeb ( )

Weighted average

N (schools)

23

95

37

264

419

School size

     

100

 

−0.270

   

150

 

−0.248

   

0.280

−0.226

−0.475

−0.540

−0.346

−0.396

−0.204

−0.392

−0.464

−0.302

−0.418

−0.182

−0.310

−0.392

0.256

−0.346

−0.160

0.228

−0.325

0.208

0.180

−0.138

0.146

0.263

0.157

−0.079

−0.116

0.063

0.205

0.104

−0.433

−0.094

−0.019

0.151

0.049

−0.879

−0.071

−0.101

0.102

−0.009

−1.420

−0.049

−0.184

0.058

−0.069

650

 

−0.027

−0.266

0.018

 

700

 

−0.005

−0.348

−0.017

 

750

 

0.017

−0.431

−0.048

 

800

 

0.039

−0.513

−0.074

 

850

 

0.061

−0.595

−0.095

 

900

 

0.083

 

−0.113

 

950

 

0.105

 

−0.125

 

1,000

 

0.127

 

−0.133

 

1,050

 

0.149

   

1,100

 

0.171

   

1,150

 

0.194

   

1,200

 

0.216

   

1,250

 

0.238

   

1,300

 

0.260

   

1,350

 

0.282

   

1,400

 

−0.304

   

Appendix 6a: Predicted Noncognitive Student Outcomes Per School Size in Secondary Education; American Studies

 

Bowen et al. ( )

Chen and Vazsonyi ( )

Dee et al. ( )

Gottfredson and DiPietro ( )

Kirkpatrick Johnson et al. ( ); middle schools

Kirkpatrick Johnson et al. ( ); high schools

N (students)

945

9,163

8,197

13,597

2,482

8,104

School size

      

100

   

−0.121

 

0.051

150

   

−0.097

0.000

0.049

200

 

0.122

0.082

−0.081

0.000

0.046

250

 

0.107

0.077

−0.067

0.000

0.044

0.219

0.093

0.072

−0.057

0.000

0.041

0.198

0.080

0.067

−0.048

0.000

0.039

0.176

0.067

0.062

−0.04

0.000

0.037

0.153

0.055

0.057

−0.033

0.000

0.034

0.128

0.044

0.052

−0.027

0.000

0.032

0.102

0.033

0.048

−0.021

0.000

0.029

0.075

0.022

0.043

−0.016

0.000

0.027

0.046

0.013

0.039

−0.012

0.000

0.024

0.016

0.004

0.035

−0.007

0.000

0.022

−0.015

−0.004

0.031

−0.003

0.000

0.019

−0.048

−0.012

0.027

0.001

0.000

0.017

−0.081

−0.019

0.023

0.004

 

0.015

−0.117

−0.025

0.019

0.007

 

0.012

−0.153

−0.031

0.016

0.011

 

0.010

−0.191

−0.036

0.013

0.014

 

0.007

−0.230

−0.041

0.009

0.017

 

0.005

−0.271

−0.044

0.006

0.019

 

0.002

1,150

−0.313

−0.047

0.003

0.022

 

0.000

1,200

−0.356

−0.050

0.001

0.024

 

−0.003

1,250

  

−0.002

0.027

 

−0.005

1,300

  

−0.005

0.029

 

−0.007

1,350

  

−0.007

0.031

 

−0.010

1,400

  

−0.009

0.033

 

−0.012

1,450

  

−0.011

0.035

 

−0.015

1,500

  

−0.013

0.037

 

−0.017

1,550

  

−0.015

0.039

 

−0.020

1,600

  

−0.017

0.041

 

−0.022

1,650

  

−0.019

0.043

 

−0.025

1,700

  

−0.020

0.045

 

−0.027

1,750

  

−0.021

0.046

 

−0.029

1,800

  

−0.022

  

−0.032

1,850

  

−0.024

  

−0.034

1,900

  

−0.024

  

−0.037

1,950

  

−0.025

  

−0.039

2,000

  

−0.026

  

−0.042

2,050

  

−0.026

  

−0.044

2,100

  

−0.027

  

−0.047

2,150

  

−0.027

  

−0.049

2,200

  

−0.027

  

−0.051

2,250

  

−0.027

  

−0.054

2,300

  

−0.027

  

−0.056

2,350

  

−0.026

  

−0.059

2,400

  

−0.026

  

−0.061

Appendix 6b: Predicted Noncognitive Student Outcomes Per School Size in Secondary Education; Studies Outside the U.S

 

Attar-Schwarz ( )

Khoury-Kassabri et al. ( )

Mooij et al. ( )

Van der Vegt et al. ( )

Vieno et al. ( )

Wei et al. ( )

N (students)

16,604

1 − 0.400

26,162

5,206

4,733

1,172

School size

      

100

−0.057

0.019

−0.057

0.047

−0.593

−0.164

150

−0.051

0.017

−0.054

0.043

−0.515

−0.158

200

−0.045

0.015

−0.050

0.040

−0.437

−0.153

250

−0.040

0.012

−0.047

0.036

−0.359

−0.147

−0.034

0.010

−0.043

0.033

−0.281

−0.142

−0.029

0.007

−0.040

0.030

−0.203

−0.136

−0.023

0.005

−0.036

0.026

−0.125

−0.130

−0.018

0.003

−0.033

0.023

−0.047

−0.125

−0.012

0.000

−0.029

0.019

0.031

−0.119

−0.006

−0.002

−0.026

0.016

0.109

−0.114

−0.001

−0.005

−0.023

0.013

0.187

−0.108

0.005

−0.007

−0.019

0.009

0.265

−0.103

0.010

−0.009

−0.016

0.006

−0.343

−0.097

0.016

−0.012

−0.012

0.002

−0.421

−0.091

0.021

−0.014

−0.009

−0.001

−0.500

−0.086

0.027

−0.016

−0.005

−0.004

−0.578

−0.08

0.033

−0.019

−0.002

−0.008

0.656

−0.075

0.038

−0.021

0.002

−0.011

0.734

−0.069

0.044

−0.024

0.005

−0.015

0.812

−0.063

0.049

−0.026

0.009

−0.018

0.890

−0.058

0.055

−0.028

0.012

−0.021

0.968

−0.052

1,150

0.060

 

0.016

−0.025

 

−0.047

1,200

0.066

 

0.019

−0.028

 

−0.041

1,250

0.072

 

0.022

−0.032

 

−0.035

1,300

  

0.026

−0.035

 

−0.030

1,350

  

0.029

−0.038

 

−0.024

1,400

  

0.033

−0.042

 

−0.019

1,450

  

0.036

−0.045

 

−0.013

1,500

  

0.040

−0.049

 

−0.008

1,550

  

0.043

−0.052

 

−0.002

1,600

  

0.047

−0.055

 

0.004

1,650

  

0.050

−0.059

 

0.009

1,700

  

0.053

−0.062

 

0.015

1,750

  

0.057

−0.066

 

0.020

1,800

  

0.060

−0.069

 

0.026

1,850

  

0.064

−0.072

 

0.032

1,900

  

0.067

−0.076

 

0.037

1,950

  

0.071

−0.079

 

0.043

2,000

  

0.074

−0.083

 

0.048

2,050

   

−0.086

 

0.054

2,100

   

−0.089

 

0.06

2,150

   

−0.093

 

0.065

2,200

   

−0.096

 

0.071

2,250

     

0.076

2,300

     

0.082

2,350

     

0.087

2,400

     

0.093

Appendix 6c: Average Outcomes Noncognitive Student Scores Per School Size in Secondary Education

 

Weighted average (all)

Weighted average (U.S.)

Weighted

average (non-U.S.)

Weighted average

(non-U.S.,

excluding Vieno et al.)

N (students)

106,765

42,488

64,277

59,544

School size

    

−0.0160

0.0284

−0.0454

−0.0294

−0.0120

0.0266

−0.0376

−0.0257

−0.0079

0.0246

−0.0294

−0.0217

−0.0041

0.0222

−0.0214

−0.0179

−0.0002

0.0198

−0.0134

−0.0140

0.0038

0.0173

−0.0052

−0.0099

0.0075

0.0149

0.0026

−0.0062

0.0113

0.0120

0.0107

−0.0022

0.0152

0.0099

0.0188

0.0016

0.0190

0.0073

0.0267

0.0055

0.0230

0.0051

0.0348

0.0093

0.0270

0.0027

0.0430

0.0135

0.0308

0.0002

0.0510

0.0173

0.0349

−0.0016

0.0590

0.0212

0.0388

−0.0038

0.0670

0.0250

0.0429

−0.0057

0.0750

0.0288

0.0469

−0.0079

0.0832

0.0329

Appendix 7a: Predicted Noncognitive School Mean s Cores Per School Size in Secondary Education; American Studies

 

Chen ( )

Chen and Vazsonyi ( )

Chen ( )

Gottfredson and DiPietro ( )

Haller ( )

Kirkpatrick Johnson et al. ( ); middle schools

Kirkpatrick Johnson et al. ( ); high schools

Klein and Cornell ( )

McNeely et al. ( )

Payne ( )

Rumberger and Palardy ( )

N (schools)

712

85

212

253

558

45

64

290

127

253

912

School size

           

100

  

0.157

−0.702

0.175

 

0.295

0.016

0.662

0.233

 

150

  

0.148

−0.565

0.164

0.000

0.281

0.015

−0.518

0.188

 

0.740

0.655

0.139

−0.467

0.154

0.000

0.267

0.015

−0.415

0.152

−0.332

0.665

−0.577

0.129

−0.391

0.144

0.000

0.253

0.014

−0.336

0.124

−0.302

−0.593

−0.501

0.120

−0.329

0.134

0.000

0.239

0.013

0.271

0.100

0.274

−0.524

−0.430

0.111

−0.277

0.124

0.000

0.225

0.012

0.216

0.079

0.246

−0.458

−0.362

0.102

−0.232

0.114

0.000

0.211

0.012

0.168

0.060

0.219

−0.394

0.297

0.093

−0.192

0.104

0.000

0.197

0.011

0.127

0.043

0.193

−0.334

0.235

0.084

−0.156

0.094

0.000

0.183

0.010

0.089

0.028

0.168

0.276

0.177

0.075

−0.124

0.084

0.000

0.168

0.010

0.055

0.015

0.144

0.221

0.123

0.066

−0.094

0.073

0.000

0.154

0.009

0.024

0.002

0.121

0.169

0.072

0.056

−0.067

0.063

0.000

0.140

0.008

−0.004

−0.009

0.098

0.120

0.024

0.047

−0.042

0.053

0.000

0.126

0.007

−0.031

−0.020

0.077

0.074

−0.020

0.038

−0.018

0.043

0.000

0.112

0.007

−0.055

−0.030

0.057

0.030

−0.061

0.029

0.003

0.033

0.000

0.098

0.006

−0.078

−0.040

0.037

−0.011

−0.099

0.020

0.024

0.023

 

0.084

0.005

−0.100

−0.049

0.019

−0.048

−0.133

0.011

0.043

0.013

 

0.070

0.004

−0.120

−0.057

0.001

−0.083

−0.163

0.002

0.062

0.003

 

0.056

0.004

−0.139

−0.066

−0.015

−0.116

−0.190

−0.007

0.079

−0.008

 

0.041

0.003

−0.158

−0.073

−0.031

−0.145

−0.214

−0.016

0.096

−0.018

 

0.027

0.002

−0.175

−0.081

−0.046

−0.171

−0.235

−0.026

0.111

−0.028

 

0.013

0.002

−0.192

−0.088

−0.059

1,150

−0.195

−0.251

−0.035

0.127

−0.038

 

−0.001

0.001

−0.208

−0.094

−0.072

1,200

−0.216

−0.265

−0.044

0.141

−0.048

 

−0.015

0.000

−0.223

−0.101

−0.084

1,250

−0.234

 

−0.053

0.155

−0.058

 

−0.029

−0.001

−0.237

−0.107

−0.095

1,300

  

−0.062

0.168

−0.068

 

−0.043

−0.001

−0.251

−0.113

−0.105

1,350

  

−0.071

0.181

−0.078

 

−0.057

−0.002

−0.265

−0.118

−0.115

1,400

  

−0.080

0.193

−0.089

 

−0.071

−0.003

−0.278

−0.124

−0.123

1,450

  

−0.089

0.205

−0.099

 

−0.086

−0.003

−0.290

−0.129

−0.130

1,500

  

−0.099

0.217

−0.109

 

−0.100

−0.004

−0.302

−0.134

−0.136

1,550

  

−0.108

0.228

−0.119

 

−0.114

−0.005

−0.314

−0.139

−0.142

1,600

  

−0.117

0.239

−0.129

 

−0.128

−0.006

−0.325

−0.144

−0.146

1,650

  

−0.126

0.249

−0.139

 

−0.142

−0.006

−0.336

−0.149

−0.150

1,700

  

−0.135

0.259

−0.149

 

−0.156

−0.007

−0.347

−0.154

−0.152

1,750

  

−0.144

0.269

−0.159

 

−0.170

−0.008

−0.357

 

−0.154

1,800

  

−0.153

 

−0.169

 

−0.184

−0.009

−0.367

 

−0.155

1,850

  

−0.162

 

−0.180

 

−0.198

−0.009

−0.377

 

−0.155

1,900

  

−0.172

 

−0.190

 

−0.213

−0.010

−0.386

 

−0.153

1,950

  

−0.181

 

−0.200

 

−0.227

−0.011

−0.396

 

−0.151

2,000

    

−0.210

 

−0.241

−0.011

−0.405

 

−0.148

2,050

    

−0.220

 

−0.255

−0.012

−0.413

 

−0.144

2,100

    

−0.230

 

−0.269

−0.013

−0.422

 

−0.140

2,150

    

−0.240

 

−0.283

−0.014

−0.430

 

−0.134

2,200

    

−0.250

 

−0.297

−0.014

−0.438

 

−0.127

2,250

    

−0.261

 

−0.311

−0.015

   

2,300

    

−0.271

 

−0.325

−0.016

   

2,350

    

−0.281

 

−0.340

−0.017

   

2,400

    

−0.291

 

−0.354

−0.017

   

Appendix 7b: Predicted Noncognitive School Mean Scores Per School Size in Secondary Education; Studies Outside the U.S

 

Attar-Schwarz ( )

Khoury-Kassabri et al. ( )

Mooij et al. ( )

Silins and Mulford ( )

Vieno et al. ( )

Wei et al. ( )

N (schools)

327

162

104

96

134

12

School size

      

100

−0.220

0.060

−0.272

−0.437

−2.967

−0.423

150

−0.198

0.053

−0.255

−0.396

−2.577

−0.408

−0.177

0.045

−0.239

−0.355

−2.187

−0.394

−0.155

0.038

−0.222

−0.314

−1.796

−0.379

−0.133

0.031

−0.206

0.272

−1.406

−0.365

−0.112

0.023

−0.189

0.231

−1.016

−0.351

−0.090

0.016

−0.173

0.190

−0.625

−0.336

−0.068

0.008

−0.156

0.149

−0.235

−0.322

−0.047

0.001

−0.140

0.108

0.155

−0.307

−0.025

−0.007

−0.124

0.067

−0.546

−0.293

−0.003

−0.014

−0.107

0.026

0.936

−0.279

0.018

−0.021

−0.091

−0.015

1.327

−0.264

0.040

−0.029

−0.074

−0.056

1.717

−0.250

0.062

−0.036

−0.058

−0.097

2.107

−0.235

0.083

−0.044

−0.041

−0.138

20498

−0.221

0.105

−0.051

−0.025

−0.179

2.888

−0.207

0.127

−0.059

−0.009

−0.220

3.278

−0.192

0.148

−0.066

0.008

−0.261

3.669

−0.178

0.170

−0.073

0.024

−0.302

4.059

−0.163

0.192

−0.081

0.041

−0.343

4.449

−0.149

0.213

−0.088

0.057

−0.384

4.84

−0.135

1,150

0.235

 

0.074

−0.425

 

−0.120

1,200

0.257

 

0.090

−0.466

 

−0.106

1,250

0.278

 

0.107

  

−0.091

1,300

  

0.123

  

−0.077

1,350

  

0.139

  

−0.063

1,400

  

0.156

  

−0.048

1,450

  

0.172

  

−0.034

1,500

  

0.189

  

−0.019

1,550

  

0.205

  

−0.005

1,600

  

0.222

  

0.009

1,650

  

0.238

  

0.024

1,700

  

0.254

  

0.038

1,750

  

0.271

  

0.053

1,800

  

0.287

  

0.067

1,850

  

−0.304

  

0.081

1,900

  

−0.320

  

0.096

1,950

  

−0.337

  

0.110

2,000

  

−0.353

  

0.125

2,050

     

0.139

2,100

     

0.153

2,150

     

0.168

2,200

     

0.182

2,250

     

0.197

2,300

     

0.211

2,350

     

0.225

2,400

     

0.240

Appendix 7c: Average Outcomes Noncognitive School Mean Scores Per School Size in Secondary Education

 

Weighted average (all)

Weighted average (U.S.)

Weighted average (non-U.S.)

Weighted average (non-U.S. excluding Vieno)

N (schools)

4346

3511

835

701

School size

    

0.151

0.283

−0.107

−0.066

0.142

0.257

−0.098

−0.060

0.134

0.231

−0.089

−0.054

0.127

0.206

−0.080

−0.049

0.120

0.181

−0.070

−0.044

0.114

0.158

−0.061

−0.038

0.109

0.135

−0.052

−0.033

0.104

0.114

−0.042

−0.028

0.100

0.093

−0.032

−0.022

0.097

0.073

−0.022

−0.017

0.094

0.054

−0.012

−0.011

0.093

0.036

−0.002

−0.005

0.092

0.018

0.008

0.000

0.091

0.002

0.019

0.005

0.092

−0.014

0.030

0.011

0.093

−0.028

0.040

0.016

0.095

−0.042

0.051

0.022

0.097

−0.055

0.062

0.027

0.101

−0.067

0.073

0.033

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Luyten, H. (2014). Quantitative Summary of Research Findings. In: School Size Effects Revisited. SpringerBriefs in Education. Springer, Cham. https://doi.org/10.1007/978-3-319-06814-5_4

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Thesis Writing: What to Write in Chapter 5

Table of contents, introduction.

This article tells what a budding researcher must include in Chapter 5-the Summary. It also includes the tense of the verb and the semantic markers, which are predominantly used in writing the summary, conclusions, and recommendations.

For others, writing Chapter 5 is the easiest part of thesis writing, but there are groups of students who would like to know more about it. If you are one of them, this article on how to write chapter 5 of your thesis is purposely written for you.

What to Write in Chapter 5

1. write the summary.

If you notice, all the parts mentioned above are already included in your Chapters 1- 4. So, the challenge is on how you are going to write and present it in Chapter 5 briefly.

Then, write sentences in  simple past  and always use  passive voice  construction rather than the active voice. You must also be familiar with the different semantic markers.

When I was enrolled in Academic Writing in my master’s degree, I learned that there are semantic markers which can be used in order not to repeat the same words or phrases such as  additionally, also, further, in addition to, moreover, contrary to, with regard to, as regards, however, finally, during the past ___ years, from 1996 to 2006, after 10 years, as shown in, as presented in, consequently, nevertheless, in fact, on the other hand, subsequently and nonetheless.

Next, you may use the following guide questions to check that you have not missed anything in writing the summary:

Finally, organize the summary of the results of your study according to the way the questions are sequenced in the statement of the problem.

2. Write the Conclusion or Conclusions

Once you have written the summary in Chapter 5, draw out a conclusion from each finding or result. It can be done per question, or you may arrange the questions per topic or sub-topic if there is any. But if your research is quantitative, answer the research question directly and tell if the hypothesis is rejected or accepted based on the findings.

As to grammar, make sure that you use the  present tense of the verb  because it comprises a general statement of the theory or the principle newly derived from the present study. So, don’t be confused because, in your summary, you use past tense, while in conclusion; you use the present tense.

3. Write the Recommendations

The recommendations must contain practical suggestions that will improve the situation or solve the problem investigated in the study.

First, it must be logical, specific, attainable, and relevant. Second, it should be addressed to persons, organizations, or agencies directly concerned with the issues or to those who can immediately implement the recommended solutions. Third, present another topic which is very relevant to the present study that can be further investigated by future researchers.

But never recommend anything that is not part of your study or not being mentioned in your findings.

Recommend nothing that is not part of your research or not being mentioned in your findings.

© 2014 July 29 M. G. Alvior | Updated 2024 January 10

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I am still having problem in organizing my summary and conclusion (my topic is dress code in public schools. to be more specific, at the Voinjama Public School. Can you help me with a sample?

Hello, Daryl. Thank you so much. About your request, I will find time to write about it. I got so busy the past months.

PLEASE HELP/GUIDE ME WHAT SHOULD I WRITE in my Chapter 5 .. your help is very much crucial as i have to submit thesis this weekend KULDEEP

Sorry, Aly. This is very late. Take your statement of the problem. the results for the statement of the problem will be the basis for your recommendation.

Hello, Jolven. Your recommendation must be based on your findings. So, if that is your title, and you found that the common causes are the ——-, then write a recommendation based on the causes.

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Quantitative Assessment of Preanalytic Variables on Clinical Evaluation of PI3/AKT/mTOR Signaling Activity in Diffuse Glioma.

Author(s): Beccari S, Mohamed E, Voong V, Hilz S, Lafontaine M, Shai A, Lim Y, Martinez J, Switzman B, Yu RL, Lupo JM, Chang EF, Hervey-Jumper SL, Berger MS, Costello JF, Phillips JJ

Publication: Mod Pathol , 2024, Vol. 37 , Page 100488

PubMed ID: 38588881 PubMed Review Paper? No

Purpose of Paper

This paper evaluated how cold ischemia time (20 min, 1 h, 6 h) affects the levels of phosphatidylinositol 3 (PI3)-kinase/AKT/mTOR (PI3K/AKT/mTOR) biomarkers in formalin-fixed, paraffin-embedded (FFPE) human high-grade diffuse glioma specimens and compared biomarker levels between FFPE glioma specimens and case-matched specimens that were snap-frozen in liquid nitrogen then thawed in cold 10% neutral buffered formalin.  Orthotopic xenograft specimens (cells from the U87 human glioblastoma cell line transplanted in a murine model) were used to investigate potential changes in PI3K/AKT/mTOR biomarker levels with cold ischemia time (0, 20 min, 1 h, 2 h, 6 h at room temperature) and with storage of slide-mounted FFPE sections for 3 or 5 months at room temperature, -20°C, and -80°C. Up to 9 tumor regions were sampled from IDH-mutant diffuse glioma tumors from eight patients during surgical resection to assess intratumoral heterogeneity; cold ischemia time of these samples was limited to ≤2 h.

Conclusion of Paper

As cold ischemia time increased, levels of p-eukaryotic initiation factor 4E-binding protein (p-4EBP1) decreased significantly in human glioblastoma (GBM) specimens, declining from expression in 100% of tumor cells after 20 min of cold ischemia on wet ice (the control) to approximately 40% and 50% of tumor cells after 1 h and 6 h at room temperature, respectively (p<0.05); the percentage of cells expressing p-ribosomal protein S6 (p-RPS6)(S240/244) was not significantly affected by the cold ischemia times investigated. Cellular expression levels of p-4EBP1 or pRPS6 (240-244) did not differ between snap-frozen specimens that were thawed in cold (4°C) 10% neutral buffered formalin and FFPE specimens that were routinely processed within 20 min of collection. The degree of intratumoral heterogeneity varied between patients, with the percentage of p-RPS6 (S240/244)-positive cells ranging between 1.62% and 55.75%.  For a given patient, the difference in p-RPS6 (S240-244) expression between samples was a poor predictor of the distance between sampling locations (R2=0.001). The number of samples collected per patient was strongly correlated with the magnitude of the difference in pRPS6 (S240/244) expression between samples (R2=0.917), indicating the extent of tumor sampling is important.  Although samples were limited to those with a cold ischemia time of ≤2 h, the percentage of cells expressing p-RPS6 was very weakly correlated to cold ischemia time (R2=0.104; p=0.029). 

When the stability of six phosphoprotein biomarkers was evaluated following cold ischemia times of 0, 20 min, 1 h, 2 h, and 6 h using the GBM orthotopic xenograft model system, significant declines in the percentage of cells expressing p-RPS6 (S240-244) and p-4EBP1 occurred after 2 h, and p-AKT after 6 h (p<0.05 for all). When the phosphoprotein intensity per cell was quantified, all six biomarkers displayed an initial increase after only 20 minutes relative to immediately processed controls (P<0.05), with p-RPS6 (240/245), p-RPS6 (235/236), and p-mechanistic target of rapamycin (p-mTOR) exhibiting subsequent declines when cold ischemia was ≥2 h. 

Changes in antigenicity were phosphoprotein-specific when slide-mounted FFPE sections of GBM orthotopic xenografts were stored for ≤3 days to 5 months at room temperature, -20°C, or -80°C. Of the three phosphoproteins analyzed (p-4EBP1, p-AKT, and p-RPS6 (240/244)), the antigenicity of p-4EBP1 and p-AKT was stable in FFPE slides stored for up 3 months regardless of temperature, but slides stored for 5 months only displayed stable immunostaining at -80°C.  Conversely, RPS6 (S240/244) immunostaining displayed reductions in the percentage of positively stained cells after 3 months of storage at -80°C.   

Study Purpose

This study evaluated how cold ischemia time (20 min, 1 h, 6 h at room temperature), defined as the time between surgical excision and preservation, affects the levels of PI3K/AKT/mTOR biomarkers in FFPE human high-grade diffuse glioma specimens and compared biomarker levels between FFPE glioma specimens and case-matched specimens that were snap-frozen in liquid nitrogen then thawed in cold 10% neutral buffered formalin. Surgically resected high-grade diffuse glioma specimens from six patients (4 were an isocitrate dehydrogenase-wildtype glioblastoma (IDH-wildtype GBM), 1 was an IDG mutant astrocytoma, and 1 was an H3K27-altered diffuse midline glioma) were divided into four pieces (25-40 mg each) on gauze moistened with saline on petri dish on wet ice.  The four specimen pieces were snap frozen in liquid nitrogen immediately or held for 20 min, 1 h, or 6 h at room temperature before being fixed in 10% neutral buffered formalin for 2 h at room temperature followed by 24 h at 4°C. Formalin-fixed specimens were transferred to cold (4°C) 70% ethanol until dehydration, clearing, and paraffin embedding. Up to 9 tumor regions were sampled from IDH-mutant diffuse glioma tumors from eight patients during surgical resection to assess intratumoral heterogeneity; cold ischemia time of these samples was limited to ≤2 h. Levels of the PI3K/AKT/mTOR biomarkers phosphor- 4E binding protein 1 (4EBP1) and p-RPS6(S24/244) were determined by immunohistochemically staining 5 µm-thick FFPE sections using an autostainer and a confocal microscope.  Results were reported as the percentage of immunopositive tumor cells and phosphoprotein intensity per cell. 

Orthotopic xenograft specimens were developed using cultured cells from the U87 human glioblastoma cell line that were transplanted in a murine model and were used to investigate potential changes in PI3K/AKT/mTOR biomarker levels with cold ischemia time (0, 20 min, 2 h, 6 h at room temperature) and with storage of slide-mounted FFPE sections for 3 or 5 months at room temperature, -20°C, and -80°C relative to controls that were sectioned ≤3 days of analysis. Xenografts of three mice per timepoint and temperature were used for analysis. Levels of PI3K/AKT/mTOR biomarkers were determined using the same workflow described above for human specimens.  

Summary of Findings:

As cold ischemia time increased, the percentage of cells expressing p-4EBP1 decreased significantly in human GBM specimens, declining from expression in 100% of tumor cells after 20 min of cold ischemia on wet ice to approximately 40% and 50% after 1 h and 6 h at room temperature, respectively (p<0.05). The percentage of cells expressing p-RPS6 (S240/244) was not significantly affected by the cold ischemia times investigated (1 h and 6 h versus 20 min controls) in human GBM specimens. The authors noted an increase in the intensity of immunostaining at the periphery of the specimen (creating an “edge effect”) with progressive cold ischemia.  When case-matched specimens that were snap-frozen in liquid nitrogen and thawed in cold (4°C) 10% neutral buffered formalin were compared to FFPE specimens that we routinely processed within 20 min of collection, no significant differences in extent or the intensity of p-4EBP1 or pRPS6 (240-244) immunostaining were observed.  

Intratumoral heterogeneity in p-RPS6 (S240/244) immunopositivity varied between patients, with differences in the percentage of immunopositive tumor cells ranging between 1.62% and 55.75%.  For a given patient, differences in the percentage of p-RPS6 (S240-244) immunopositive cells were a poor predictor of the distance between samples (R2=0.001).  The number of samples collected per patient was strongly correlated with the magnitude of the difference in pRPS6 (S240/244) staining between samples (R2=0.917), indicating that the extent of tumor sampling is important.  Although samples were limited to those with a cold ischemia time of ≤2 h, the percentage of cells that were immunopositive for p-RPS6 was very weakly correlated to cold ischemia time (R2=0.104; p=0.029). 

When the stability of six phosphoprotein biomarkers was evaluated following cold ischemia times of 0, 20 min, 1 h, 2 h, and 6 h using the GBM orthotopic xenograft model system, significant declines in the percentage of cells expressing p-RPS6 (S240-244) and p-4EBP1 occurred after 2 h, and p-AKT after 6 h. However, when the phosphoprotein intensity per cell was quantified, all six biomarkers displayed an increase after only 20 minutes relative to immediately processed controls (P<0.05), with p-RPS6 (240/245), p-RPS6 (235/236), and p-mTOR exhibiting subsequent declines in specimens with ischemia ≥2 h. All six biomarkers also displayed more intense immunostaining at the periphery of the tumor (but not normal adjacent tissue) after a cold ischemia time of 2 h, which contributed to an increase in variability within a section. 

When slide-mounted FFPE sections of GBM orthotopic xenografts were stored for ≤3 days to 5 months at room temperature, -20°C, or -80°C, changes in antigenicity were phosphoprotein-specific. Of the three phosphoproteins analyzed (p-4EBP1, p-AKT, and p-RPS6 (240/244)), the antigenicity of p-4EBP1 and p-AKT were stable in FFPE slides stored for up 3 months regardless of temperature, but slides stored for 5 months only displayed stable immunostaining for the two antigens when storage was at -80°C.  Conversely, RPS6 (S240/244) immunostaining displayed reductions in the percentage of positively stained cells after 3 months of storage at -80°C.   

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Biospecimens

  • Tissue - Brain

Preservative Types

  • Neoplastic - Mixed type
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Protein Immunofluorescence assay
Protein Immunohistochemistry

Pre-analytical Factors:

ClassificationPre-analytical FactorValue(s)
20 min (on wet ice)
1 h (at room temperature)
6 h (at room temperature)
Intratumoral sampling (exact positions not specified)
Formalin (buffered)
Snap frozen
≤3 d
3 months
5 months
Room temperature
-20°C
-80°C

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    The conclusions are as stated below: i. Students' use of language in the oral sessions depicted their beliefs and values. based on their intentions. The oral sessions prompted the students to be ...

  2. A Practical Guide to Writing Quantitative and Qualitative Research

    In quantitative research, ... On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies (non-directional hypothesis) ... In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based ...

  3. Dissertation Results/Findings Chapter (Quantitative)

    Learn how to write the results chapter for quantitative dissertations and theses, including what to include, how to structure it and tips and tricks. The results chapter presents the data using a clear text narrative, supported by tables, graphs and charts.

  4. 9 Presenting the Results of Quantitative Analysis

    9 Presenting the Results of Quantitative Analysis . Mikaila Mariel Lemonik Arthur. This chapter provides an overview of how to present the results of quantitative analysis, in particular how to create effective tables for displaying quantitative results and how to write quantitative research papers that effectively communicate the methods used and findings of quantitative analysis.

  5. How to Write a Results Section

    Reporting quantitative research results. If you conducted quantitative research, you'll likely be working with the results of some sort of statistical analysis.. Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables.It should also state whether or not each hypothesis was supported.

  6. Chapter 14: Completing 'Summary of findings' tables and ...

    Learn how to create and use 'Summary of findings' tables to present the main findings of a systematic review, including the certainty or quality of evidence. The chapter also explains the GRADE approach for assessing the certainty of evidence across five domains.

  7. Draft the Summary of Findings

    Learn how to draft the summary of findings for your dissertation, differentiating between your version of truth and literal truth. Find out how to assert, discuss, and compare your findings with the literature review and the research questions.

  8. Research Summary

    Learn how to write a research summary for a quantitative study, including its structure, purpose, and characteristics. See an example of a research summary on the effects of yoga on mental health.

  9. Writing Quantitative Research Studies

    Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to ...

  10. What Is Quantitative Research?

    Learn what quantitative research is, how to collect and analyze numerical data, and what are its advantages and disadvantages. Find examples of quantitative research methods, statistics and questions.

  11. How to Write the Dissertation Findings or Results

    1. Reporting Quantitative Findings. The best way to present your quantitative findings is to structure them around the research hypothesis or questions you intend to address as part of your dissertation project. Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

  12. How To Write A Research Summary

    So, follow the steps below to write a research summary that sticks. 1. Read the parent paper thoroughly. You should go through the research paper thoroughly multiple times to ensure that you have a complete understanding of its contents. A 3-stage reading process helps.

  13. Research Findings

    Qualitative Findings. Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants ...

  14. 7.1 Reading results in quantitative research

    Quantitative results. Quantitative articles often contain tables, and scanning them is a good way to begin reading the results. A table usually provides a quick, condensed summary of the report's key findings. Tables are a concise way to report large amounts of data.

  15. PDF Chapter 4 Quantitative Summary of Research Findings

    Chapter 4. antitative Summary of Research FindingsHans LuytenThis chapter presents a quantitative summary of research with regard to the effects of school size on student achievement and noncognitive outcomes (such as involvement, partic. pation, social cohesion, safety, attendance, etc.). The non-cognitive outcomes are widely considered as ...

  16. Chapter Four: Quantitative Methods (Part 3

    For quantitative research, you should do your literature review before coming up with your research questions/hypotheses. Your questions and hypotheses should flow from the literature. ... You should include the following elements in your discussion section: (a) a summary of your findings, (b) implications, (c) limitations, and (d) future ...

  17. 16. Reporting quantitative results

    Execute a quantitative research report using key elements for accuracy and openness. So you've completed your quantitative analyses and are ready to report your results. We're going to spend some time talking about what matters in quantitative research reports, but the very first thing to understand is this: openness with your data and ...

  18. Data Analysis in Research

    Future Research: Suggest areas for further investigation. Importance of Data analysis in Research. Data analysis is a fundamental component of the research process. Here are five key points highlighting its importance: Enhances Accuracy and Reliability Data analysis ensures that research findings are accurate and reliable.

  19. Chapter Summary

    Chapter Summary. Chapter 7 • Quantitative Research Methods. Quantitative research relies on the collection and analysis of numerical data to describe, explain, predict, or control variables of interest. Quantitative research focuses on objectivity that permits the researcher to generalize findings beyond a particular situation or setting.

  20. Quantitative Summary of Research Findings

    With regard to school size research, providing a quantitative summary of research findings is therefore quite complicated. Often more than just two school size categories are compared. In addition, the categories used vary between studies. In other cases the relation between school size and outcomes is modeled as a mathematical function (mostly ...

  21. What to Write in Chapter 5: 3 Important Tips

    Once you have written the summary in Chapter 5, draw out a conclusion from each finding or result. It can be done per question, or you may arrange the questions per topic or sub-topic if there is any. But if your research is quantitative, answer the research question directly and tell if the hypothesis is rejected or accepted based on the findings.

  22. Chapter 5 SUMMARY OF FINDINGS, CONCLUSION AND RECCOMENDATION

    This document provides guidelines for summarizing findings, drawing conclusions, and making recommendations from a research study. It discusses that a summary should briefly outline the purpose, participants, time period, and research method used. Findings should address each research question while presenting generalizations and statistics.

  23. PDF CHAPTER 6 Summary of findings, conclusions and recommendations

    The presses publish research monographs, undergraduate texts, school textbooks, professional books, trade books, reference works and research journals. The main publishing categories are undergraduate textbooks and research monographs. Table 5.4 gives the summary of categories published by each press. 5. Are they actually sold, if so in what ...

  24. PDF CHAPTER 5 Summary, findings, conclusions and recommendations

    Summary, findings, conclusions and recommendations 5.1 INTRODUCTION This chapter presents the summary of the findings, conclusions and recommendations based on the ... The research approach used in this study was a quantitative, cross-sectional research approach (Polit & Hungler 1991:195).

  25. Quantitative Assessment of Preanalytic Variables on Clinical Evaluation

    Summary of Findings: As cold ischemia time increased, the percentage of cells expressing p-4EBP1 decreased significantly in human GBM specimens, declining from expression in 100% of tumor cells after 20 min of cold ischemia on wet ice to approximately 40% and 50% after 1 h and 6 h at room temperature, respectively (p<0.05).